Showing papers presented at "Congress on Evolutionary Computation in 2001"

Particle swarm optimization: developments, applications and resources

Abstract: This paper focuses on the engineering and computer science aspects of developments, applications, and resources related to particle swarm optimization. Developments in the particle swarm algorithm since its origin in 1995 are reviewed. Included are brief discussions of constriction factors, inertia weights, and tracking dynamic systems. Applications, both those already developed, and promising future application areas, are reviewed. Finally, resources related to particle swarm optimization are listed, including books, Web sites, and software. A particle swarm optimization bibliography is at the end of the paper.

Fuzzy adaptive particle swarm optimization

Abstract: A fuzzy system is implemented to dynamically adapt the inertia weight of the particle swarm optimization algorithm (PSO). Three benchmark functions with asymmetric initial range settings are selected as the test functions. The same fuzzy system has been applied to all three test functions with different dimensions. The experimental results illustrate that the fuzzy adaptive PSO is a promising optimization method, which is especially useful for optimization problems with a dynamic environment.

Tracking and optimizing dynamic systems with particle swarms

Abstract: Using particle swarms to track and optimize dynamic systems is described. Issues related to tracking and optimizing dynamic systems are briefly reviewed. Three kinds of dynamic systems are defined for the purposes of this paper. One of them is chosen for preliminary analysis using the particle swarm on the parabolic benchmark function. Successful tracking of a 10-dimensional parabolic function with a severity of up to 1.0 is demonstrated. A number of issues related to tracking and optimizing dynamic systems with particle swarms are identified. Directions for future research and applications are suggested.

PDE: a Pareto-frontier differential evolution approach for multi-objective optimization problems

Abstract: The use of evolutionary algorithms (EAs) to solve problems with multiple objectives (known as multi-objective optimization problems (MOPs)) has attracted much attention. Being population based approaches, EAs offer a means to find a group of Pareto-optimal solutions in a single run. Differential evolution (DE) is an EA that was developed to handle optimization problems over continuous domains. The objective of this paper is to introduce a novel Pareto-frontier differential evolution (PDE) algorithm to solve MOPs. The solutions provided by the proposed algorithm for two standard test problems, outperform the Strength Pareto Evolutionary Algorithm, one of the state-of-the-art evolutionary algorithms for solving MOPs.

MBO: marriage in honey bees optimization-a Haplometrosis polygynous swarming approach

Abstract: Honey-bees are one of the most well studied social insects. They exhibit many features that distinguish their use as models for intelligent behavior. These features include division of labor, communication on the individual and group level, and cooperative behavior. In this paper, we present a unified model for the marriage in honey-bees within an optimization context. The model simulates the evolution of honey-bees starting with a solitary colony (single queen without a family) to the emergence of an eusocial colony (one or more queens with a family). From optimization point of view, the model is a committee machine approach where we evolve solutions using a committee of heuristics. The model is applied to a fifty propositional satisfiability problems (SAT) with 50 variables and 215 constraints to guarantee that the problems are centered on the phase transition of 3-SAT. Our aim in this paper is to analyze the behavior of the algorithm using biological concepts (number of queens, spermatheca size, and number of broods) rather than trying to improve the performance of the algorithm while losing the underlying biological essence. Notwithstanding, the algorithm outperformed WalkSAT, one of the state-of-the-art algorithms for SAT.

Failure of Pareto-based MOEAs: does non-dominated really mean near to optimal?

Abstract: Many multi-objective evolutionary algorithms (MOEAs) have been proposed over the years. The main part of the most successful algorithms such as PESA, or NSGA-II, are the Pareto based selection strategy that decide survivors using dominance among individuals. However, does the Pareto based selection strategy always succeed in finding the Pareto optimal solutions? This paper shows a very simple example that can cause serious trouble for the Pareto based MOEAs. In such an instance, various solutions, which are apart from the true Pareto-optimums, are left as hardly-dominated solutions. We define such solutions as dominance resistant solutions (DRSs), and show a class of problems which produces DRSs easily. To cope with this difficulty we propose the /spl alpha/-domination strategy that relaxes the domination introducing a weak trade-off among objectives. With the /spl alpha/-domination strategy, the DRSs are effectively purged from the population.

Scaling up fast evolutionary programming with cooperative coevolution

Abstract: Evolutionary programming (EP) has been applied with success to many numerical and combinatorial optimization problems in recent years. However, most analytical and experimental results on EP have been obtained using low-dimensional problems. It is interesting to know whether the empirical results obtained from the low-dimensional problems still hold for high-dimensional cases. It was discovered that neither classical EP (CEP) nor fast EP (FEP) performed satisfactorily for some large-scale problems. The paper shows empirically that FEP with cooperative coevolution (FEPCC) can speed up convergence rates on the large-scale problems whose dimension ranges from 100 to 1000. Cooperative coevolution adopts the divide-and-conquer strategy. It divides the system into many modules, and evolves each module separately and cooperatively. The results of FEPCC on the problems investigated here are something of a surprise. The time used by FEPCC to find a near optimal solution appears to scale linearly; that is, the time used seems to go up linearly as the dimensionality of the problems studied increases.

Multiobjective genetic programming: reducing bloat using SPEA2

Abstract: This study investigates the use of multiobjective techniques in genetic programming (GP) in order to evolve compact programs and to reduce the effects caused by bloating. The proposed approach considers the program size as a second, independent objective besides the program functionality. In combination with a multiobjective evolutionary technique, SPEA2, this method outperforms four other strategies to reduce bloat with regard to both convergence speed and size of the produced programs on an even-parity problem.

Towards an artificial immune system for network intrusion detection: an investigation of clonal selection with a negative selection operator

Abstract: The paper describes research towards the use of an artificial immune system (AIS) for network intrusion detection. Specifically, we focus on one significant component of a complete AIS, static clonal selection with a negative selection operator, describing this system in detail. Three different data sets from the UCI repository for machine learning are used in the experiments. Two important factors, the detector sample size and the antigen sample size, are investigated in order to generate an appropriate mixture of general and specific detectors for learning non-self antigen patterns. The results of series of experiments suggest how to choose appropriate detector and antigen sample sizes. These ideal sizes allow the AIS to achieve a good non-self antigen detection rate with a very low rate of self antigen detection. We conclude that the embedded negative selection operator plays an important role in the AIS by helping it to maintain a low false positive detection rate.

Inferring a system of differential equations for a gene regulatory network by using genetic programming

Abstract: Describes an evolutionary method for identifying a gene regulatory network from the observed time series data of the gene's expression. We use a system of ordinary differential equations as a model of the network and infer their right-hand sides by using genetic programming (GP). To explore the search space more effectively in the course of evolution, the least mean squares (LMS) method is used along with ordinary GP. We apply our method to three target networks and empirically show how successfully GP infers the systems of differential equations.

Representing classification problems in genetic programming

Abstract: Five alternative methods are proposed to perform multi-class classification tasks using genetic programming. These methods are: (1) binary decomposition, in which the problem is decomposed into a set of binary problems and standard genetic programming methods are applied; (2) static range selection, where the set of real values returned by a genetic program is divided into class boundaries using arbitrarily-chosen division points; (3) dynamic range selection, in which a subset of training examples are used to determine where, over the set of reals, class boundaries lie; (4) class enumeration, which constructs programs similar in syntactic structure to a decision tree; and (5) evidence accumulation, which allows separate branches of the program to add to the certainty of any given class. The results show that the dynamic range selection method is well-suited to the task of multi-class classification and is capable of producing classifiers that are more accurate than the other methods tried when comparable training times are allowed. The accuracy of the generated classifiers was comparable to alternative approaches over several data sets.

The advantages of generative grammatical encodings for physical design

Abstract: One of the applications of evolutionary algorithms is the automatic creation of designs. For evolutionary techniques to scale to the complexities necessary for actual engineering problems, it has been argued that generative systems, where the genotype is an algorithm for constructing the final design, should be used as the encoding. We describe a system for creating generative specifications by combining Lindenmayer systems with evolutionary algorithms, and we apply it to the problem of generating table designs. Designs evolved by our system reach an order of magnitude more parts than previous generative systems. Comparing it against a non-generative encoding, we find that the generative system produces designs with higher fitness and is faster than the non-generative system. Finally, we demonstrate the ability of our system to go from design to manufacture by constructing evolved table designs using rapid prototyping equipment.

Parallel quantum-inspired genetic algorithm for combinatorial optimization problem

Abstract: This paper proposes a new parallel evolutionary algorithm called parallel quantum-inspired genetic algorithm (PQGA). Quantum-inspired genetic algorithm (QGA) is based on the concept and principles of quantum computing such as qubits and superposition of states. Instead of binary, numeric, or symbolic representation, by adopting the qubit chromosome as a representation, QGA can represent a linear superposition of solutions due to its probabilistic representation. QGA is suitable for parallel structures because of rapid convergence and good global search capability. That is, QGA is able to possess the two characteristics of exploration and exploitation simultaneously. The effectiveness and the applicability of PQGA are demonstrated by experimental results on the knapsack problem, which is a well-known combinatorial optimization problem. The results show that PQGA is superior to QGA as well as other conventional genetic algorithms.

Comparison between Genetic Network Programming (GNP) and Genetic Programming (GP)

Abstract: Recently, many methods of evolutionary computation such as genetic algorithm (GA) and genetic programming (GP) have been developed as a basic tool for modeling and optimizing of complex systems. Generally speaking, GA has the genome of a string structure, while the genome in GP is the tree structure. Therefore, GP is suitable for constructing complicated programs, which can be applied to many real world problems. However, GP might sometimes be difficult to search for a solution because of its bloat. A novel evolutionary method named Genetic Network Programming (GNP), whose genome is a network structure is proposed to overcome the low searching efficiency of GP and is applied to the problem of the evolution of ant behavior in order to study the effectiveness of GNP. In addition, the comparison of the performances between GNP and GP is carried out in simulations on ant behaviors.

A particle swarm optimization for reactive power and voltage control in electric power systems

Abstract: This paper presents a particle swarm optimization (PSO) for reactive power and voltage control (RPVC) in electric power systems. RPVC can be formulated as a mixed-integer nonlinear optimization problem (MINLP). The proposed method expands the original PSO to handle a MINLP and determines an RPVC strategy with continuous and discrete control variables such as automatic voltage regulator (AVR) operating values of generators, tap positions of on-load tap changer (OLTC) of transformers, and the amount of reactive power compensation equipment (RPCE). The feasibility of the proposed method is demonstrated and compared with reactive tabu search (RTS) and the enumeration method on practical power system models with promising results.

An efficient genetic algorithm with less fitness evaluation by clustering

Abstract: To solve a general problem with genetic algorithms, it is desirable to maintain the population size as large as possible. In some cases, however, the cost to evaluate each individual is relatively high, and it is difficult to maintain a large population. To solve this problem, we propose a hybrid GA based on clustering, which considerably reduces the evaluation number without any loss of performance. The algorithm divides the whole population into several clusters, and evaluates only one representative for each cluster. The fitness values of other individuals are estimated from the representative fitness values indirectly, which can maintain a large population with less number of evaluations. Several benchmark tests have been conducted and the results show that the proposed GA is very efficient.

A hardware implementation of the Compact Genetic Algorithm

Abstract: We propose a hardware implementation of the Compact Genetic Algorithm (GA). The design is realized using the Verilog hardware description language (HDL) and then fabricated on an FPGA. Our design, though simple, runs about 1000 times faster than the software executing on a workstation. An alternative hardware for linkage learning is also proposed in order to enhance the capability of the Compact GA to solve highly deceptive problems.

Learning fuzzy cognitive maps using evolution strategies: a novel schema for modeling and simulating high-level behavior

Abstract: Fuzzy cognitive maps (FCMs) are recognized as a flexible and powerful modeling and simulating technique. However, it is a relatively new methodology, which exhibits weaknesses mainly in the algorithmic background. Such weaknesses become evident during heuristic evaluations of the cause-effect relationships describing FCM-based systems. External intervention (typically from experts) for the determination and fine-tuning of the FCM parameters cannot be regarded as an accurate and efficient way to design and manage FCMs, especially in the case of highly complicated structures, where even experts meet difficulties in their attempts at a holistic interpretation. The introduction and implementation of a training procedure based on a robust and flexible optimization tool constitutes a promising alternative. This study focuses on evolutionary computation, since this domain encompasses optimization techniques possessing the needed features for this type of problems. Evolution strategies (ESs) appear to be the most appropriate methodology and, as such, they are tested in this work for a potential implementation in FCM-based systems. The proposed approach combines FCM and ES concepts and sets the basis for the establishment and deployment of structural evolution, which broadens the applicability of FCMs.

Evolutionary algorithms for multi-objective optimization: performance assessments and comparisons

Abstract: The rapid advances of evolutionary methods for multi-objective (MO) optimization poses the difficulty of keeping track of the developments in this field as well as selecting an appropriate evolutionary approach that best suits the problem in-hand. This paper aims to analyze the strength and weakness of different evolutionary methods proposed in the literature. For this purpose, ten existing well-known evolutionary MO approaches have been experimented and compared extensively on two benchmark problems with different MO optimization difficulties and characteristics. Besides considering the usual two important aspects of MO performance, i.e., the spread across the Pareto-optimal front as well as the ability to attain the global optimum or final trade-offs, this paper also proposes a few useful performance measures for better and comprehensive examination of each approach both quantitatively and qualitatively. Simulation results for the comparisons are commented and summarized.

A crossover operator using independent component analysis for real-coded genetic algorithms

Abstract: For real-coded genetic algorithms, there have been proposed many crossover operators. The blend crossover (BLX-/spl alpha/) proposed by L.J. Eshelman and J.D. Schaffer (1993) shows a good searching ability for separable fitness functions. However, because of its component-wise operation, BLX-/spl alpha/ faces difficulties in the optimization of non-separable fitness functions. This paper proposes a novel crossover operator that combines the BLX-/spl alpha/ with independent component analysis (ICA). By applying the ICA to the population, the coordinate system of the search space is transformed so as to increase the separability of the fitness function, and then the BLX-/spl alpha/ is applied. A computer simulation shows the good searching ability of the proposed method for non-separable fitness functions.

Managing approximate models in evolutionary aerodynamic design optimization

Abstract: Approximate models have to be used in evolutionary optimization when the original fitness function is computationally very expensive. Unfortunately, the convergence property of the evolutionary algorithm is unclear when an approximate model is used for fitness evaluation because approximation errors are involved in the model. What is worse, the approximate model may introduce false optima that lead the evolutionary algorithm to a wrong solution. To address this problem, individual and generation based evolution control are introduced to ensure that the evolutionary algorithm using approximate fitness functions will converge correctly. A framework for managing approximate models in generation-based evolution control is proposed. This framework is well suited for parallel evolutionary optimization in which evaluation of the fitness function is time-consuming. Simulations on two benchmark problems and one example of aerodynamic design optimization demonstrate that the proposed algorithm is able to achieve a correct solution as well as a significantly reduced computation time.

GA automated design and synthesis of analog circuits with practical constraints

Abstract: The paper develops a genetic algorithm (GA) based "growing" technique to design and synthesise analogue circuits with practical constraints, such as the manufacturer's preferred component values. Most existing problems when evolutionary search techniques are applied to circuit design are addressed. The developed GA technique is then applied both to synthesise the topology of a network and perform value optimisation on the components based on a set of commonly used component values (E-12 series). Passive filter networks synthesised this way are realisable, effective and of novel topology. It is anticipated that this technique can be extended to active networks.

An evolutionary approach for the tuning of a chess evaluation function using population dynamics

Abstract: Using the game of chess, we propose an approach for the tuning of evaluation function parameters based on evolutionary algorithms. We introduce an iterative method for population member selection and show how the resulting win, loss, or draw information from competition can be used in conjunction with the statistical analysis of the population to develop evaluation function parameter values. A population of evaluation function candidates are randomly generated and exposed to the proposed learning techniques. An analysis to the success of learning is given and the undeveloped and developed players are examined through competition against a commercial chess program.

Fitness evaluation for nurse scheduling problems

Abstract: When applying evolutionary algorithms to difficult real-world problems, the fitness function routinely needs evaluating for a very high number of intermediary cases. The paper is concerned with real-world nurse rostering problems with highly constrained resources. We consider a particular approach, which allows for a quick evaluation and is general enough to deal with other kinds of resource planning problems with time-related constraints. The model developed for this approach handles the constraints in a modular way and the addition of new constraints is relatively straightforward. Simple constraints (such as those affecting the personal wishes of employees) and global constraints (such as balancing the workload among people) can be formulated easily using this approach. Our approach can also handle very complex time-related constraints as well as conditions that are related to previously planned work. Moreover, it provides clear feedback about violation of constraints. The approach has been implemented successfully in a nurse rostering program entitled "Plane" which is used in hospitals all over Belgium. It can tackle a high number of specific and modifiable constraints of a very different nature. The benefits from this approach (in terms of software requirements) are small memory use and a computationally simple, single evaluation function allowing for the simultaneous rostering of several hospital wards at the same time.

A study on non-random mating and varying population size in genetic algorithms using a royal road function

Abstract: We present a study on the effects of non-random mating and varying population size in genetic algorithm (GA) performance. We tested two algorithms: the non-incest genetic algorithm with varying population size (niGAVaPS) and the negative assortative mating genetic algorithm with varying population size (nAMGAVaPS), on a royal road function. These algorithms mimic natural species behavior by selecting parents according to parenthood (niGAVaPS) or phenotype similarity (nAMGAVaPS). We show that both algorithms outperform simple GA in the example shown. The results suggest that this may be due to the fact that genetic diversity is kept at a higher level by niGAVaPS and nAMGAVaPS, preventing the premature convergence of the algorithms to local optima.

Thresholding-a selection operator for noisy ES

Abstract: The starting point for the analysis and experiments presented in this paper is a simplified elevator control problem, called 'S-ring'. As in many other real-world optimization problems, the exact fitness function evaluation is disturbed by noise. Evolution strategies (ES) can generally cope with noisy fitness function values. It has been proposed that the 'plus'-strategy can find better solutions by keeping over-valued function values, thus preventing inferior offspring with fitness inflated by noise from being accepted. The 'plus'-strategy builds an implicit barrier around the current best population. We propose to make this barrier building process explicit and to employ a threshold value /spl tau/ to be used in a selection operator for noisy fitness functions. 'Thresholding' accepts a new individual if its apparent fitness is better than that of the parent by at least the margin /spl tau/. First analytical investigations and empirical results from tests on the sphere-model and 'S-ring' are presented.

Netcrawling-optimal evolutionary search with neutral networks

Abstract: Several studies have demonstrated that in the presence of a high degree of selective neutrality, in particular on fitness landscapes featuring neutral networks, evolution is qualitatively different from that of the more common model of rugged/correlated fitness landscapes often (implicitly) assumed by GA researchers. We characterise evolutionary dynamics on fitness landscapes with neutral networks and argue that, if a certain correlation-like statistical property holds, the most efficient strategy for evolutionary search is not population-based, but rather a population-of-one netcrawler-a variety of hill-climber. We derive quantitative estimates for expected waiting times to discovery of fitter genotypes and discuss implications for evolutionary algorithm design, including a proposal for an adaptive variant of the netcrawler.

Adaptive encoding for aerodynamic shape optimization using evolution strategies

Abstract: The evaluation of fluid dynamic properties of various different structures is a computationally very demanding process. This is of particular importance when population based evolutionary algorithms are used for the optimization of aerodynamic structures like wings or turbine blades. Besides choosing algorithms which only need few generations or function evaluations, it is important to reduce the number of object parameters as much as possible. This is usually done by restricting the optimization to certain attributes of the design which are seen as important. By doing so, the freedom for the optimization is restricted to areas of the design space where good solutions are expected. This can be problematic especially if the properties of the design and their interactions are not known sufficiently well like for example for transonic flow conditions. In order to be able to combine the conflicting constraints of a minimal set of parameters and the maximal degree of freedom, we propose an adaptive or growing representation for spline coded structures. In this way, the optimization is started with a simple representation with a minimal description length. The number of describing parameters is adapted during the optimization using a mutation operator working on the structure of the encoding. We compare this method with four different evolution strategies using a spline fitting problem as a test function.

On neutral networks and evolvability

Abstract: Evolutionary algorithms apply the processes of variation, reproduction and selection to look for an individual that is capable of solving the task in hand. In order to improve the evolvability of a population, we propose to copy important characteristics of nature's search space. Desired characteristics for a genotype-phenotype mapping are described, and several highly redundant genotype-phenotype mappings are analyzed in the context of a population-based search. We show that evolvability is influenced by the existence of neutral networks in the genotype space. The extent of the neutral networks affects the interconnectivity of the search space and thereby affects evolvability. Species evolving on a non-redundant mapping reach a state of stasis after a few generations; in effect, evolution comes to a halt. However, species evolving on a genotype-phenotype mapping with extensive neutral networks are continuously able to find adaptive mutations and are able to locate higher optima. The existence of highly intertwined neutral networks increases the evolvability of a population.

A swarm with an effective information sharing mechanism for unconstrained and constrained single objective optimisation problems

Abstract: We present an effective multilevel information sharing strategy within a swarm to handle single objective, constrained and unconstrained optimization problems. A swarm is considered as a collection of individuals having a common goal to reach the best value (minimum or maximum) of a function. The success of a swarm is attributed to the identification of a set of competent leaders and a meaningful information sharing scheme between the leaders and the rest of the individuals that enables the swarm to collectively attain the common goal. The proposed algorithm mimics the above behavioral processes of a real swarm and maintains unique individuals at all time instants. The uniqueness among the individuals result in a set of near optimal solutions at the final phase that is useful for sensitivity analysis. The benefits of the effective information sharing strategy is illustrated by solving two unconstrained problems with multiple equal and unequal optima and a constrained optimization problem.