Showing papers in "IEEE Transactions on Evolutionary Computation in 2012"

Cooperatively Coevolving Particle Swarms for Large Scale Optimization

Abstract: This paper presents a new cooperative coevolving particle swarm optimization (CCPSO) algorithm in an attempt to address the issue of scaling up particle swarm optimization (PSO) algorithms in solving large-scale optimization problems (up to 2000 real-valued variables). The proposed CCPSO2 builds on the success of an early CCPSO that employs an effective variable grouping technique random grouping. CCPSO2 adopts a new PSO position update rule that relies on Cauchy and Gaussian distributions to sample new points in the search space, and a scheme to dynamically determine the coevolving subcomponent sizes of the variables. On high-dimensional problems (ranging from 100 to 2000 variables), the performance of CCPSO2 compared favorably against a state-of-the-art evolutionary algorithm sep-CMA-ES, two existing PSO algorithms, and a cooperative coevolving differential evolution algorithm. In particular, CCPSO2 performed significantly better than sep-CMA-ES and two existing PSO algorithms on more complex multimodal problems (which more closely resemble real-world problems), though not as well as the existing algorithms on unimodal functions. Our experimental results and analysis suggest that CCPSO2 is a highly competitive optimization algorithm for solving large-scale and complex multimodal optimization problems.

Using the Averaged Hausdorff Distance as a Performance Measure in Evolutionary Multiobjective Optimization

Abstract: The Hausdorff distance dH is a widely used tool to measure the distance between different objects in several research fields. Possible reasons for this might be that it is a natural extension of the well-known and intuitive distance between points and/or the fact that dH defines in certain cases a metric in the mathematical sense. In evolutionary multiobjective optimization (EMO) the task is typically to compute the entire solution set-the so-called Pareto set-respectively its image, the Pareto front. Hence, dH should, at least at first sight, be a natural choice to measure the performance of the outcome set in particular since it is related to the terms spread and convergence as used in EMO literature. However, so far, dH does not find the general approval in the EMO community. The main reason for this is that dH penalizes single outliers of the candidate set which does not comply with the use of stochastic search algorithms such as evolutionary strategies. In this paper, we define a new performance indicator, Δp, which can be viewed as an “averaged Hausdorff distance” between the outcome set and the Pareto front and which is composed of (slight modifications of) the well-known indicators generational distance (GD) and inverted generational distance (IGD). We will discuss theoretical properties of Δp (as well as for GD and IGD) such as the metric properties and the compliance with state-of-theart multiobjective evolutionary algorithms (MOEAs), and will further on demonstrate by empirical results the potential of Δp as a new performance indicator for the evaluation of MOEAs.

A Fast Way of Calculating Exact Hypervolumes

Abstract: We describe a new algorithm WFG for calculating hypervolume exactly. WFG is based on the recently-described observation that the exclusive hypervolume of a point p relative to a set S is equal to the difference between the inclusive hypervolume of p and the hypervolume of S with each point limited by the objective values in p. WFG applies this technique iteratively over a set to calculate its hypervolume. Experiments show that WFG is substantially faster (in five or more objectives) than all previously-described algorithms that calculate hypervolume exactly.

Differential Evolution With Neighborhood Mutation for Multimodal Optimization

Abstract: In this paper, a neighborhood mutation strategy is proposed and integrated with various niching differential evolution (DE) algorithms to solve multimodal optimization problems. Although variants of DE are highly effective in locating a single global optimum, no DE variant performs competitively when solving multi-optima problems. In the proposed neighborhood based differential evolution, the mutation is performed within each Euclidean neighborhood. The neighborhood mutation is able to maintain the multiple optima found during the evolution and evolve toward the respective global/local optimum. To test the performance of the proposed neighborhood mutation DE, a total of 29 problem instances are used. The proposed algorithms are compared with a number of state-of-the-art multimodal optimization approaches and the experimental results suggest that although the idea of neighborhood mutation is simple, it is able to provide better and more consistent performance over the state-of-the-art multimodal algorithms. In addition, a comparative survey on niching algorithms and their applications are also presented.

A Multiobjective Genetic Algorithm to Find Communities in Complex Networks

Abstract: A multiobjective genetic algorithm to uncover community structure in complex network is proposed. The algorithm optimizes two objective functions able to identify densely connected groups of nodes having sparse inter-connections. The method generates a set of network divisions at different hierarchical levels in which solutions at deeper levels, consisting of a higher number of modules, are contained in solutions having a lower number of communities. The number of modules is automatically determined by the better tradeoff values of the objective functions. Experiments on synthetic and real life networks show that the algorithm successfully detects the network structure and it is competitive with state-of-the-art approaches.

Combining Multiobjective Optimization With Differential Evolution to Solve Constrained Optimization Problems

Abstract: During the past decade, solving constrained optimization problems with evolutionary algorithms has received considerable attention among researchers and practitioners. Cai and Wang's method (abbreviated as CW method) is a recent constrained optimization evolutionary algorithm proposed by the authors. However, its main shortcoming is that a trial-and-error process has to be used to choose suitable parameters. To overcome the above shortcoming, this paper proposes an improved version of the CW method, called CMODE, which combines multiobjective optimization with differential evolution to deal with constrained optimization problems. Like its predecessor CW, the comparison of individuals in CMODE is also based on multiobjective optimization. In CMODE, however, differential evolution serves as the search engine. In addition, a novel infeasible solution replacement mechanism based on multiobjective optimization is proposed, with the purpose of guiding the population toward promising solutions and the feasible region simultaneously. The performance of CMODE is evaluated on 24 benchmark test functions. It is shown empirically that CMODE is capable of producing highly competitive results compared with some other state-of-the-art approaches in the community of constrained evolutionary optimization.

Decomposition-Based Multiobjective Evolutionary Algorithm With an Ensemble of Neighborhood Sizes

Abstract: The multiobjective evolutionary algorithm based on decomposition (MOEA/D) has demonstrated superior performance by winning the multiobjective optimization algorithm competition at the CEC 2009. For effective performance of MOEA/D, neighborhood size (NS) parameter has to be tuned. In this letter, an ensemble of different NSs with online self-adaptation is proposed (ENS-MOEA/D) to overcome this shortcoming. Our experimental results on the CEC 2009 competition test instances show that an ensemble of different NSs with online self-adaptation yields superior performance over implementations with only one fixed NS.

Real-Coded Chemical Reaction Optimization

Abstract: Optimization problems can generally be classified as continuous and discrete, based on the nature of the solution space. A recently developed chemical-reaction-inspired metaheuristic, called chemical reaction optimization (CRO), has been shown to perform well in many optimization problems in the discrete domain. This paper is dedicated to proposing a real-coded version of CRO, namely, RCCRO, to solve continuous optimization problems. We compare the performance of RCCRO with a large number of optimization techniques on a large set of standard continuous benchmark functions. We find that RCCRO outperforms all the others on the average. We also propose an adaptive scheme for RCCRO which can improve the performance effectively. This shows that CRO is suitable for solving problems in the continuous domain.

A General Framework of Multipopulation Methods With Clustering in Undetectable Dynamic Environments

Abstract: To solve dynamic optimization problems, multiple population methods are used to enhance the population diversity for an algorithm with the aim of maintaining multiple populations in different subareas in the fitness landscape. Many experimental studies have shown that locating and tracking multiple relatively good optima rather than a single global optimum is an effective idea in dynamic environments. However, several challenges need to be addressed when multipopulation methods are applied, e.g., how to create multiple populations, how to maintain them in different subareas, and how to deal with the situation where changes cannot be detected or predicted. To address these issues, this paper investigates a hierarchical clustering method to locate and track multiple optima for dynamic optimization problems. To deal with undetectable dynamic environments, this paper applies the random immigrants method without change detection based on a mechanism that can automatically reduce redundant individuals in the search space throughout the run. These methods are implemented into several research areas, including particle swarm optimization, genetic algorithm, and differential evolution. An experimental study is conducted based on the moving peaks benchmark to test the performance with several other algorithms from the literature. The experimental results show the efficiency of the clustering method for locating and tracking multiple optima in comparison with other algorithms based on multipopulation methods on the moving peaks benchmark.

The Automatic Design of Multiobjective Ant Colony Optimization Algorithms

Abstract: Multiobjective optimization problems are problems with several, typically conflicting, criteria for evaluating solutions. Without any a priori preference information, the Pareto optimality principle establishes a partial order among solutions, and the output of the algorithm becomes a set of nondominated solutions rather than a single one. Various ant colony optimization (ACO) algorithms have been proposed in recent years for solving such problems. These multiobjective ACO (MOACO) algorithms exhibit different design choices for dealing with the particularities of the multiobjective context. This paper proposes a formulation of algorithmic components that suffices to describe most MOACO algorithms proposed so far. This formulation also shows that existing MOACO algorithms often share equivalent design choices, but they are described in different terms. Moreover, this formulation is synthesized into a flexible algorithmic framework, from which not only existing MOACO algorithms may be instantiated, but also combinations of components that were never studied in the literature. In this sense, this paper goes beyond proposing a new MOACO algorithm, but it rather introduces a family of MOACO algorithms. The flexibility of the proposed MOACO framework facilitates the application of automatic algorithm configuration techniques. The experimental results presented in this paper show that the automatically configured MOACO framework outperforms the MOACO algorithms that inspired the framework itself. This paper is also among the first to apply automatic algorithm configuration techniques to multiobjective algorithms.

Multiobjective Evolutionary Algorithms in Aeronautical and Aerospace Engineering

Abstract: Nowadays, the solution of multiobjective optimization problems in aeronautical and aerospace engineering has become a standard practice. These two fields offer highly complex search spaces with different sources of difficulty, which are amenable to the use of alternative search techniques such as metaheuristics, since they require little domain information to operate. From the several metaheuristics available, multiobjective evolutionary algorithms (MOEAs) have become particularly popular, mainly because of their availability, ease of use, and flexibility. This paper presents a taxonomy and a comprehensive review of applications of MOEAs in aeronautical and aerospace design problems. The review includes both the characteristics of the specific MOEA adopted in each case, as well as the features of the problems being solved with them. The advantages and disadvantages of each type of approach are also briefly addressed. We also provide a set of general guidelines for using and designing MOEAs for aeronautical and aerospace engineering problems. In the final part of the paper, we provide some potential paths for future research, which we consider promising within this area.

A Filter Approach to Multiple Feature Construction for Symbolic Learning Classifiers Using Genetic Programming

Abstract: Feature construction is an effort to transform the input space of classification problems in order to improve the classification performance Feature construction is particularly important for classifier inducers that cannot transform their input space intrinsically This paper proposes GPMFC, a multiple-feature construction system for classification problems using genetic programming (GP) This paper takes a nonwrapper approach by introducing a filter-based measure of goodness for constructed features The constructed, high-level features are functions of original input features These functions are evolved by GP using an entropy-based fitness function that maximizes the purity of class intervals A decomposable objective function is proposed so that the system is able to construct multiple high-level features for each problem The constructed features are used to transform the original input space to a new space with better separability Extensive experiments are conducted on a number of benchmark problems and symbolic learning classifiers The results show that, in most cases, the new approach is highly effective in increasing the classification performance in rule-based and decision tree classifiers The constructed features help improve the learning performance of symbolic learners The constructed features, however, may lack intelligibility

A Novel Immune Clonal Algorithm for MO Problems

Abstract: Research on multiobjective optimization (MO) becomes one of the hot points of intelligent computation. Compared with evolutionary algorithm, the artificial immune system used for solving MO problems (MOPs) has shown many good performances in improving the convergence speed and maintaining the diversity of the antibody population. However, the simple clonal selection computation has some difficulties in handling some more complex MOPs. In this paper, the simple clonal selection strategy is improved and a novel immune clonal algorithm (NICA) is proposed. The improvements in NICA are mainly focus on four aspects. 1) Antibodies in the antibody population are divided into dominated ones and nondominated ones, which is suitable for the characteristic of one multiobjective optimization problem has a series Pareto-optimal solutions. 2) The entire cloning is adopted instead of different antibodies having different clonal rate. 3) The clonal selection is based on the Pareto-dominance and one antibody is selected or not depending on whether it is a nondominated one, which is different from the traditional clonal selection manner. 4) The antibody population updating operation after the clonal selection is adopted, which makes antibody population under a certain size and guarantees the convergence of the algorithm. The influences of the main parameters are analyzed empirically. Compared with the existed algorithms, simulation results on MOPs and constrained MOPs show that NICA in most problems is able to And much better spread of solutions and better convergence near the true Pareto-optimal front.

Continuous Dynamic Constrained Optimization—The Challenges

Abstract: Many real-world dynamic problems have constraints, and in certain cases not only the objective function changes over time, but also the constraints. However, there is no research in answering the question of whether current algorithms work well on continuous dynamic constrained optimization problems (DCOPs), nor is there any benchmark problem that reflects the common characteristics of continuous DCOPs. This paper contributes to the task of closing this gap. We will present some investigations on the characteristics that might make DCOPs difficult to solve by some existing dynamic optimization (DO) and constraint handling (CH) algorithms. We will then introduce a set of benchmark problems with these characteristics and test several representative DO and CH strategies on these problems. The results confirm that DCOPs do have special characteristics that can significantly affect algorithm performance. The results also reveal some interesting observations where the presence or combination of different types of dynamics and constraints can make the problems easier to solve for certain types of algorithms. Based on the analyses of the results, a list of potential requirements that an algorithm should meet to solve DCOPs effectively will be proposed.

Performance Evaluation of Evolutionary Algorithms for Optimal Filter Design

Abstract: In analog filter design, component values are selected due to manufactured constant values where performing an exhaustive search on all possible combinations of preferred values for obtaining an optimized design is not feasible. The application of evolutionary algorithms (EA) in analog active filter circuit design and optimization is a promising area which is based on concepts of natural selection and survival of the fittest. In this paper, the performances of genetic algorithm, artificial bee colony optimization, and particle swarm optimization, which are nature-inspired EA techniques, are evaluated for active filter design. Each algorithm is applied to two different filter structures and performances of them are also evaluated when filter design is realized with components selected from different manufactured series.

A Study of Collapse in Bare Bones Particle Swarm Optimization

Abstract: The dynamic update rule of particle swarm optimization is formulated as a second-order stochastic difference equation and general relations are derived for search focus, search spread, and swarm stability at stagnation. The relations are applied to three particular particle swarm optimization (PSO) implementations, the standard PSO of Clerc and Kennedy, a PSO with discrete recombination, and the Bare Bones swarm. The simplicity of the Bare Bones swarm facilitates theoretical analysis and a further no-collapse condition is derived. A series of experimental trials confirms that Bare Bones situated at the edge of collapse is comparable to other PSOs, and that performance can be still further improved with the use of an adaptive distribution. It is conjectured that, subject to spread, stability and no-collapse, there is a single encompassing particle swarm paradigm, and that an important aspect of parameter tuning within any particular manifestation is to remove any deleterious behavior that ensues from the dynamics.

Preference-Based Solution Selection Algorithm for Evolutionary Multiobjective Optimization

Abstract: Since multiobjective evolutionary algorithms (MOEAs) provide a set of nondominated solutions, decision making of selecting a preferred one out of them is required in real applications. However, there has been some research on MOEA in which the user's preferences are incorporated for this purpose. This paper proposes preference-based solution selection algorithm (PSSA) by which user can select a preferred one out of nondominated solutions obtained by any one of MOEAs. The PSSA, which is a kind of multiple criteria decision making (MCDM) algorithm, represents user's preference to multiple objectives or criteria as a degree of consideration by fuzzy measure and globally evaluates obtained solutions by fuzzy integral. The PSSA is also employed in each and every generation of evolutionary process to propose multiobjective quantum-inspired evolutionary algorithm with preference-based selection (MQEA-PS). To demonstrate the effectiveness of PSSA and MQEA-PS, computer simulations and real experiments on evolutionary multiobjective optimization for the fuzzy path planner of mobile robot are carried out. Computer simulation and experiment results show that the user's preference is properly reflected in the selected solution. Moreover, MQEA-PS shows improved performance for the DTLZ problems and fuzzy path planner optimization problem compared to MQEA with dominance-based selection and other MOEAs like NSGA-II and MOPBIL.

On Gradients and Hybrid Evolutionary Algorithms for Real-Valued Multiobjective Optimization

Abstract: Algorithms that make use of the gradient, i.e., the direction of maximum improvement, to search for the optimum of a single-objective function have been around for decades. They are commonly accepted to be important and have been applied to tackle single-objective optimization problems in many fields. For multiobjective optimization problems, much less is known about the gradient and its algorithmic use. In this paper, we aim to contribute to the understanding of gradients for numerical, i.e., real-valued, multiobjective optimization. Specifically, we provide an analytical parametric description of the set of all nondominated, i.e., most promising, directions in which a solution can be moved such that the objective values either improve or remain the same. This result completes previous work where this set is described only for one particular case, namely when some of the nondominated directions have positive, i.e., nonimproving, components and the final set can be computed by taking the subset of directions that are all nonpositive. In addition we use our result to assess the utility of using gradient information for multiobjective optimization where the goal is to obtain a Pareto set of solutions that approximates the optimal Pareto set. To this end, we design and consider various multiobjective gradient-based optimization algorithms. One of these algorithms uses the description of the multiobjective gradient provided here. Also, we hybridize an existing multiobjective evolutionary algorithm (MOEA) with the various multiobjective gradient-based optimization algorithms. During optimization, the performance of the gradient-based optimization algorithms is monitored and the available computational resources are redistributed to allow the (currently) most effective algorithm to spend the most resources. We perform an experimental analysis using a few well-known benchmark problems to compare the performance of different optimization methods. The results underline that the use of a population of solutions that is characteristic of MOEAs is a powerful concept if the goal is to obtain a good Pareto set, i.e., instead of only a single solution. This makes it hard to increase convergence speed in the initial phase using gradient information to improve any single solution. However, in the longer run, the use of gradient information does ultimately allow for better fine-tuning of the results and thus better overall convergence.

Grammatical Evolution of Local Search Heuristics

Abstract: Genetic programming approaches have been employed in the literature to automatically design constructive heuristics for cutting and packing problems. These heuristics obtain results superior to human-created constructive heuristics, but they do not generally obtain results of the same quality as local search heuristics, which start from an initial solution and iteratively improve it. If local search heuristics can be successfully designed through evolution, in addition to a constructive heuristic which initializes the solution, then the quality of results which can be obtained by automatically generated algorithms can be significantly improved. This paper presents a grammatical evolution methodology which automatically designs good quality local search heuristics that maintain their performance on new problem instances.

An Efficient Resource Allocation Scheme Using Particle Swarm Optimization

Abstract: Developing techniques for optimal allocation of limited resources to a set of activities has received increasing attention in recent years. In this paper, an efficient resource allocation scheme based on particle swarm optimization (PSO) is developed. Different from many existing evolutionary algorithms for solving resource allocation problems (RAPs), this PSO algorithm incorporates a novel representation of each particle in the population and a comprehensive learning strategy for the PSO search process. The novelty of this representation lies in that the position of each particle is represented by a pair of points, one on each side of the constraint hyper-plane in the problem space. The line joining these two points intersects the constraint hyper-plane and their intersection point indicates a feasible solution. With the evaluation value of the feasible solution used as the fitness value of the particle, such a representation provides an effective way to ensure the equality resource constraints in RAPs are met. Without the distraction of infeasible solutions, the particle thus searches the space smoothly. In addition, particles search for optimal solutions by learning from themselves and their neighborhood using the comprehensive learning strategy, helping prevent premature convergence and improve the solution quality for multimodal problems. This new algorithm is shown to be applicable to both single-objective and multiobjective RAPs, with performance validated by a number of benchmarks and by a real-world bed capacity planning problem. Experimental results verify the effectiveness and efficiency of the proposed algorithm.

Hybrid Metaheuristics Based on Evolutionary Algorithms and Simulated Annealing: Taxonomy, Comparison, and Synergy Test

Abstract: The design of hybrid metaheuristics with ideas taken from the simulated annealing and evolutionary algorithms fields is a fruitful research line. In this paper, we first present an overview of the hybrid metaheuristics based on simulated annealing and evolutionary algorithms presented in the literature and classify them according to two well-known taxonomies of hybrid methods. Second, we perform an empirical study comparing the behavior of a representative set of the hybrid approaches based on evolutionary algorithms and simulated annealing found in the literature. In addition, a study of the synergy relationships provided by these hybrid approaches is presented. Finally, we analyze the behavior of the best performing hybrid metaheuristic with regard to several state-of-the-art evolutionary algorithms for binary combinatorial problems. The experimental studies presented provide useful conclusions about the schemes for combining ideas from simulated annealing and evolutionary algorithms that may improve the performance of these kinds of approaches and suggest that these hybrids metaheuristics represent a competitive alternative for binary combinatorial problems.

Evolution of Plastic Learning in Spiking Networks via Memristive Connections

Abstract: This paper presents a spiking neuroevolutionary system which implements memristors as plastic connections, i.e., whose weights can vary during a trial. The evolutionary design process exploits parameter self-adaptation and variable topologies, allowing the number of neurons, connection weights, and interneural connectivity pattern to emerge. By comparing two phenomenological real-world memristor implementations with networks comprised of: 1) linear resistors, and 2) constant-valued connections, we demonstrate that this approach allows the evolution of networks of appropriate complexity to emerge whilst exploiting the memristive properties of the connections to reduce learning time. We extend this approach to allow for heterogeneous mixtures of memristors within the networks; our approach provides an in-depth analysis of network structure. Our networks are evaluated on simulated robotic navigation tasks; results demonstrate that memristive plasticity enables higher performance than constant-weighted connections in both static and dynamic reward scenarios, and that mixtures of memristive elements provide performance advantages when compared to homogeneous memristive networks.

On the Impact of Mutation-Selection Balance on the Runtime of Evolutionary Algorithms

Abstract: The interplay between mutation and selection plays a fundamental role in the behavior of evolutionary algorithms (EAs). However, this interplay is still not completely understood. This paper presents a rigorous runtime analysis of a non-elitist population-based EA that uses the linear ranking selection mechanism. The analysis focuses on how the balance between parameter η, controlling the selection pressure in linear ranking, and parameter χ controlling the bit-wise mutation rate, impacts the runtime of the algorithm. The results point out situations where a correct balance between selection pressure and mutation rate is essential for finding the optimal solution in polynomial time. In particular, it is shown that there exist fitness functions which can only be solved in polynomial time if the ratio between parameters η and χ is within a narrow critical interval, and where a small change in this ratio can increase the runtime exponentially. Furthermore, it is shown quantitatively how the appropriate parameter choice depends on the characteristics of the fitness function. In addition to the original results on the runtime of EAs, this paper also introduces a very useful analytical tool, i.e., multi-type branching processes, to the runtime analysis of non-elitist population-based EAs.

Promoting Creative Design in Interactive Evolutionary Computation

Abstract: We use a new measure of creativity as a guide in an interactive evolutionary art task and tie the results to natural language usage of the term “creative.” Following previous work, we explore a tractable definition of creativity, one emphasizing the novelty of systems, and its addition to an interactive application. We next introduce a generative ecosystemic art system, EvoEco, an agent-based pixel-level means of generating images. EvoEco is used as a component of an online survey which asks users to evolve a pleasing image and then rank the success of the process and its output. Evolutionary search is augmented with the creativity measure, and compared with control groups augmented with either random search or a measure of phenotypic distance. We show that users consistently rate the creativity measure-enhanced version as more “creative” and “novel” than other search techniques. We further derive additional insights into appropriate forms of genetic representation and pattern space-traversal in an interactive evolutionary algorithm.

Effects of Iterated Interactions in Multiplayer Spatial Evolutionary Games

Abstract: Mechanisms promoting the evolution of cooperation in two players and two strategies (22) evolutionary games have been investigated in great detail over the past decades. Understanding the effects of repeated interactions in multiplayer spatial games, however, is a formidable challenge. In this paper, we present a multiplayer evolutionary game model in which agents play iterative games in spatial populations. -player versions of the well-known Prisoner's Dilemma and the Snowdrift games are used as the basis of the investigation. These games were chosen as they have emerged as the most promising mathematical metaphors for studying cooperative phenomena. Here, we have adopted an experimental approach to study the emergent behavior, exploring different parameter configurations via numerical simulations. Key model parameters include the cost-to-benefit ratio, the size of groups, the number of repeated encounters, and the interaction topology. Our simulation results reveal that, while the introduction of iterated interactions does promote higher levels of cooperative behavior across a wide range of parameter settings, the cost-to-benefit ratio and group size are important factors in determining the appropriate length of beneficial repeated interactions. In particular, increasing the number of iterated interactions may have a detrimental effect when the cost-to-benefit ratio and group size are small.

Progressive Alignment Method Using Genetic Algorithm for Multiple Sequence Alignment

Abstract: In this paper, we have proposed a progressive alignment method using a genetic algorithm for multiple sequence alignment, named GAPAM. We have introduced two new mechanisms to generate an initial population: the first mechanism is to generate guide trees with randomly selected sequences and the second is shuffling the sequences inside such trees. Two different genetic operators have been implemented with GAPAM. To test the performance of our algorithm, we have compared it with existing well-known methods, such as PRRP, CLUSTALX, DIALIGN, HMMT, SB_PIMA, ML_PIMA, MULTALIGN, and PILEUP8, and also other methods, based on genetic algorithms (GA), such as SAGA, MSA-GA, and RBT-GA, by solving a number of benchmark datasets from BAliBase 2.0. To make a fairer comparison with the GA based algorithms such as MSA-GA and RBT-GA, we have performed further experiments covering all the datasets reported by those two algorithms. The experimental results showed that GAPAM achieved better solutions than the others for most of the cases, and also revealed that the overall performance of the proposed method outperformed the other methods mentioned above.

An Artificial Immune System for Classification With Local Feature Selection

Abstract: A new multiclass classifier based on immune system principles is proposed. The unique feature of this classifier is the embedded property of local feature selection. This method of feature selection was inspired by the binding of an antibody to an antigen, which occurs between amino acid residues forming an epitope and a paratope. Only certain selected residues (so-called energetic residues) take part in the binding. Antibody receptors are formed during the clonal selection process. Antibodies binding (recognizing) with most antigens (instances) create an immune memory set. This set can be reduced during an optional apoptosis process. Local feature selection and apoptosis result in data-reduction capabilities. The amount of data required for classification was reduced by up to 99%. The classifier has only two user-settable parameters controlling the global-local properties of the feature space searching. The performance of the classifier was tested on several benchmark problems. The comparative tests were performed using k-NN, support vector machines, and random forest classifiers. The obtained results indicate good performance of the proposed classifier in comparison with both other immune inspired classifiers and other classifiers in general.

A Multiobjective Evolutionary Algorithm That Diversifies Population by Its Density

Abstract: Most existing multiobjective evolutionary algorithms (MOEAs) assume the existence of Pareto-optimal solutions/Pareto-optimal objective vectors in a neighborhood of an obtained Pareto-optimal set (PS)/Pareto-optimal front (PF). Obviously, this assumption does not work well on the multiobjective problem (MOP) whose true PF and true PS are in the form of multiple segments-truly disconnected MOP (TYD-MOP). Moreover, these MOEAs commonly involve more than three control parameters; and some of them even involve nine control parameters. The stabilities of their performance against parameter settings are generally unknown. In this paper, we propose a MOEA, namely multiobjective density driven evolutionary algorithm (MODdEA), which can handle TYD-MOP. MODdEA stores all evaluated solutions by a binary space partitioning (BSP) tree. Benefiting from the BSP scheme, a fast solution density estimation by the archive is naturally obtained. MODdEA uses this estimated density together with the nondominated rank to probabilistically select mating individuals, which relaxes the neighborhood assumption on PF in a parameter-less manner. Moreover, two genetic operators, extended arithmetic crossover and diversified mutation, are proposed to enhance the explorative search ability of the algorithm. MODdEA is examined on two test problem sets. The first test set consists of six TYD-MOPs; the second test set consists of 17 benchmark MOPs which are commonly examined by the existing MOEAs. Comparing to 14 test MOEAs, MODdEA has superior performance on TYD-MOP and is competitive on MOP whose true PF and PS are one single connected segment.

Adaptation in Dynamic Environments: A Case Study in Mission Planning

Abstract: Many random events usually are associated with executions of operational plans at various companies and organizations. For example, some tasks might be delayed and/or executed earlier. Some operational constraints can be introduced due to new regulations or business rules. In some cases, there might be a shift in the relative importance of objectives associated with these plans. All these potential modifications create a huge pressure on planning staff for generating plans that can adapt quickly to changes in environment during execution. In this paper, we address adaptation in dynamic environments. Many researchers in evolutionary community addressed the problem of optimization in dynamic environments. Through an overview on applying evolutionary algorithms for solving dynamic optimization problems, we classify the paper into two main categories: 1) finding/tracking optima, and 2) adaptation and we discuss their relevance for solving planning problems. Based on this discussion, we propose a computational approach to adaptation within the context of planning. This approach models the dynamic planning problem as a multiobjective optimization problem and an evolutionary mechanism is incorporated; this adapts the current solution to new situations when a change occurs. As the multiobjective model is used, the proposed approach produces a set of non-dominated solutions after each planning cycle. This set of solutions can be perceived as an information-rich data set which can be used to support the adaptation process against the effect of changes. The main question is how to exploit this set efficiently. In this paper, we propose a method based on the concept of centroids over a number of changing-time steps; at each step we obtain a set of non-dominated solutions. We carried out a case study on this proposed approach. Mission planning was used for our experiments and experimental analysis. We selected mission planning as our test environment because battlefields are always highly dynamic and uncertain and can be conveniently used to demonstrate different types of changes, especially time-varying constraints. The obtained results support the significance of our centroid-based approach.

Maximum Satisfiability: Anatomy of the Fitness Landscape for a Hard Combinatorial Optimization Problem

Abstract: The fitness landscape of MAX-3-SAT is investigated for random instances above the satisfiability phase transition. This paper includes a scaling analysis of the time to reach a local optimum, the number of local optima, the expected probability of reaching a local optimum as a function of its fitness, the expected fitness found by local search and the best fitness, the probability of reaching a global optimum, the size and relative positions of the global optima, the mean distance between the local and global optima, the expected fitness as a function of the Hamming distance from an optimum and their basins of attraction. These analyses show why the problem becomes hard for local search algorithms as the system size increases. The paper also shows how a recently proposed algorithm can exploit long-range correlations in the fitness landscape to improve on the state-of-the-art heuristic algorithms.