Showing papers presented at "Congress on Evolutionary Computation in 2009"

The performance of a new version of MOEA/D on CEC09 unconstrained MOP test instances

Abstract: This paper describes the idea of MOEA/D and proposes a strategy for allocating the computational resource to different subproblems in MOEA/D. The new version of MOEA/D has been tested on all the CEC09 unconstrained MOP test instances.

Comparing parameter tuning methods for evolutionary algorithms

Abstract: Tuning the parameters of an evolutionary algorithm (EA) to a given problem at hand is essential for good algorithm performance. Optimizing parameter values is, however, a non-trivial problem, beyond the limits of human problem solving.In this light it is odd that no parameter tuning algorithms are used widely in evolutionary computing. This paper is meant to be stepping stone towards a better practice by discussing the most important issues related to tuning EA parameters, describing a number of existing tuning methods, and presenting a modest experimental comparison among them. The paper is concluded by suggestions for future research - hopefully inspiring fellow researchers for further work.

GPU-based parallel particle swarm optimization

Abstract: A novel parallel approach to run standard particle swarm optimization (SPSO) on Graphic Processing Unit (GPU) is presented in this paper. By using the general-purpose computing ability of GPU and based on the software platform of Compute Unified Device Architecture (CUDA) from NVIDIA, SPSO can be executed in parallel on GPU. Experiments are conducted by running SPSO both on GPU and CPU, respectively, to optimize four benchmark test functions. The running time of the SPSO based on GPU (GPU-SPSO) is greatly shortened compared to that of the SPSO on CPU (CPU-SPSO). Running speed of GPU-SPSO can be more than 11 times as fast as that of CPU-SPSO, with the same performance. compared to CPU-SPSO, GPU-SPSO shows special speed advantages on large swarm population applications and hign dimensional problems, which can be widely used in real optimizing problems.

Evolving coordinated quadruped gaits with the HyperNEAT generative encoding

Abstract: Legged robots show promise for complex mobility tasks, such as navigating rough terrain, but the design of their control software is both challenging and laborious. Traditional evolutionary algorithms can produce these controllers, but require manual decomposition or other problem simplification because conventionally-used direct encodings have trouble taking advantage of a problem's regularities and symmetries. Such active intervention is time consuming, limits the range of potential solutions, and requires the user to possess a deep understanding of the problem's structure. This paper demonstrates that HyperNEAT, a new and promising generative encoding for evolving neural networks, can evolve quadruped gaits without an engineer manually decomposing the problem. Analyses suggest that HyperNEAT is successful because it employs a generative encoding that can more easily reuse phenotypic modules. It is also one of the first neuroevolutionary algorithms that exploits a problem's geometric symmetries, which may aid its performance. We compare HyperNEAT to FT-NEAT, a direct encoding control, and find that HyperNEAT is able to evolve impressive quadruped gaits and vastly outperforms FT-NEAT. Comparative analyses reveal that HyperNEAT individuals are more holistically affected by genetic operators, resulting in better leg coordination. Overall, the results suggest that HyperNEAT is a powerful algorithm for evolving control systems for complex, yet regular, devices, such as robots.

Dynamic optimization using Self-Adaptive Differential Evolution

Abstract: In this paper we investigate a Self-Adaptive Differential Evolution algorithm (jDE) where F and CR control parameters are self-adapted and a multi-population method with aging mechanism is used. The performance of the jDE algorithm is evaluated on the set of benchmark functions provided for the CEC 2009 special session on evolutionary computation in dynamic and uncertain environments.

Overcoming the bootstrap problem in evolutionary robotics using behavioral diversity

Abstract: The bootstrap problem is often recognized as one of the main challenges of evolutionary robotics: if all individuals from the first randomly generated population perform equally poorly, the evolutionary process won't generate any interesting solution. To overcome this lack of fitness gradient, we propose to efficiently explore behaviors until the evolutionary process finds an individual with a non-minimal fitness. To that aim, we introduce an original diversity-preservation mechanism, called behavioral diversity, that relies on a distance between behaviors (instead of genotypes or phenotypes) and multi-objective evolutionary optimization. This approach has been successfully tested and compared to a recently published incremental evolution method (multi-subgoal evolution) on the evolution of a neuro-controller for a light-seeking mobile robot. Results obtained with these two approaches are qualitatively similar although the introduced one is less directed than multi-subgoal evolution.

Tackling high dimensional nonseparable optimization problems by cooperatively coevolving particle swarms

Abstract: This paper attempts to address the question of scaling up Particle Swarm Optimization (PSO) algorithms to high dimensional optimization problems. We present a cooperative coevolving PSO (CCPSO) algorithm incorporating random grouping and adaptive weighting, two techniques that have been shown to be effective for handling high dimensional nonseparable problems. The proposed CCPSO algorithms out-performed a previously developed coevolving PSO algorithm on nonseparable functions of 30 dimensions. Furthermore, the scalability of the proposed algorithm to high dimensional nonseparable problems (of up to 1000 dimensions) is examined and compared with two existing coevolving Differential Evolution (DE) algorithms, and new insights are obtained. Our experimental results show the proposed CCPSO algorithms can perform reasonably well with only a small number of evaluations. The results also suggest that both the random grouping and adaptive weighting schemes are viable approaches that can be generalized to other evolutionary optimization methods.

Multi-objective optimization using self-adaptive differential evolution algorithm

Abstract: In this paper, we propose a Multiobjective Self-adaptive Differential Evolution algorithm with objective-wise learning strategies (OW-MOSaDE) to solve numerical optimization problems with multiple conflicting objectives. The proposed approach learns suitable crossover parameter values and mutation strategies for each objective separately in a multi-objective optimization problem. The performance of the proposed OW-MOSaDE algorithm is evaluated on a suit of 13 benchmark problems provided for the CEC2009 MOEA Special Session and Competition (http://www3.ntu.edu.sg/home/epnsugan/) on Performance Assessment of Constrained / Bound Constrained Multi-Objective Optimization Algorithms.

A clustering particle swarm optimizer for dynamic optimization

Abstract: In the real world, many applications are nonstationary optimization problems. This requires that optimization algorithms need to not only find the global optimal solution but also track the trajectory of the changing global best solution in a dynamic environment. To achieve this, this paper proposes a clustering particle swarm optimizer (CPSO) for dynamic optimization problems. The algorithm employs hierarchical clustering method to track multiple peaks based on a nearest neighbor search strategy. A fast local search method is also proposed to find the near optimal solutions in a local promising region in the search space. Six test problems generated from a generalized dynamic benchmark generator (GDBG) are used to test the performance of the proposed algorithm. The numerical experimental results show the efficiency of the proposed algorithm for locating and tracking multiple optima in dynamic environments.

Detecting change in dynamic fitness landscapes

Abstract: Change detection enables an evolutionary algorithm operating in a dynamic environment to respond with undertaking necessary steps for maintaining its performance. We consider two major types of change detection, population-based and sensor-based. For population-based we show its relation to statistical hypothesis testing and analyze it using receiver-operating characteristics. For sensor-based the relationship between detection success and number of employed sensors is studied and the dimensionality problem is addressed. Finally, we discuss how both types of change detection compare to each other.

A cooperative coevolutionary algorithm with Correlation based Adaptive Variable Partitioning

Abstract: A cooperative coevolutionary algorithm (CCEA) is an extension to an evolutionary algorithm (EA); it employs a divide and conquer strategy to solve an optimization problem. In its basic form, a CCEA splits the variables of an optimization problem into multiple smaller subsets and evolves them independently in different subpopulations. The dynamics of a CCEA is far more complex than an EA and its performance can vary from good to bad depending on the separability of the optimization problem. This paper provides some insights into why CCEA in its basic form is not suitable for nonseparable problems and introduces a Cooperative Coevolutionary Algorithm with Correlation based Adaptive Variable Partitioning (CCEA-AVP) to deal with such problems. The performance of CCEA-AVP is compared with CCEA and EA to highlight its benefits. CCEA-AVP offers the possibility to deal with problems where separability among variables might vary in different regions of the search space.

Bare Bones Particle Swarm Optimization with Gaussian or Cauchy jumps

Abstract: Bare Bones Particle Swarm Optimization (BBPSO) is a powerful algorithm, which has shown potential to solving multimodal optimization problems. Unfortunately, BBPSO may also get stuck into local optima when optimizing functions with many local optima in high dimensional search space. In previous attempts an approach was developed which consists of a jump strategy combined with PSO in order to escape from local optima and promising results have been obtained. In this paper, we combine BBPSO with a jump strategy when no fitness improvement is observed. The jump strategy is implemented based on the Gaussian or the Cauchy probability distribution. The algorithm was tested on a suite of well-known benchmark multimodal functions and the results were compared with those obtained by the standard BBPSO algorithm and with BBPSO with re-initialization. Simulation results show that the BBPSO with the jump strategy performs well in all functions investigated. We also notice that the improved performance is due to a successful number of Gaussian or Cauchy jumps.

Quantifying ruggedness of continuous landscapes using entropy

Abstract: A major unsolved problem in the field of optimisation and computational intelligence is how to determine which algorithms are best suited to solving which problems. This research aims to analytically characterise individual problems as a first step towards attempting to link problem types with the algorithms best suited to solving them. In particular, an information theoretic technique for analysing the ruggedness of a fitness landscape with respect to neutrality was adapted to work in continuous landscapes and to output a single measure of ruggedness. Experiments run on test functions with increasing ruggedness show that the proposed measure of ruggedness produced relative values consistent with a visual inspection of the problem landscapes. Combined with other measures of complexity, the proposed ruggedness measure could be used to more broadly characterise the complexity of fitness landscapes in continuous domains.

Service Value Networks

Abstract: The current industry-driven trend of providing flexible e-services lays the ground for the new research area ``service value networks'' (SVNs). We observe a rising number of industry-oriented publications provided by research departments of large companies such as IBM or SAP as well as the fact that more and more IS conferences offer special tracks on that issue. However, when it comes to formalizing and economically analyzing such SVNs that offer joint complex services to service customers, scientific approaches are in their infancy. We intend to fill this research gap by providing a clear understanding of service value networks by defining their characteristics, their structure, and their components. Mapping these aspects into a formalized model, we intend to establish a reference point for future work in the area of service value networks.

Performance assessment of DMOEA-DD with CEC 2009 MOEA competition test instances

Abstract: In this paper, the DMOEA-DD, which is an improvement of DMOEA[1, 2] by using domain decomposition technique, is applied to tackle the CEC 2009 MOEA competition test instances that are multiobjective optimization problems (MOPs) with complicated Pareto set (PS) geometry shapes. The performance assessment is given by using IGD [3, 4] as performance metric.

The multiobjective evolutionary algorithm based on determined weight and sub-regional search

Abstract: By dividing the multiobjective optimization of the decision space into several small regions, this paper proposes multi-objective optimization algorithm based on sub-regional search, which makes individuals in same region operate each other by evolutionary operator and the information between the individuals of different regions exchange through their offsprings re-divided into regions again. Since the proposed algorithm utilizes the sub-regional search, the computational complexity at each generation is lower than the NSGA-II and MSEA. The proposed algorithm makes use of the max-min strategy with determined weight as fitness functions, which make it approach evenly distributed solution in Pareto front. This paper presents a kind of easy technology dealing with the constraint, which makes the proposed algorithm solved unconstrained multiobjective problems can also be used to solve constrained multiobjective problems. The numerical results, with 13 unconstrained multiobjective optimization testing instances and 10 constrained multiobjective optimization testing instances, are shown in this paper.

Local search based evolutionary multi-objective optimization algorithm for constrained and unconstrained problems

Abstract: Evolutionary multi-objective optimization algorithms are commonly used to obtain a set of non-dominated solutions for over a decade. Recently, a lot of emphasis have been laid on hybridizing evolutionary algorithms with MCDM and mathematical programming algorithms to yield a computationally efficient and convergent procedure. In this paper, we test an augmented local search based EMO procedure rigorously on a test suite of constrained and unconstrained multi-objective optimization problems. The success of our approach on most of the test problems not only provides confidence but also stresses the importance of hybrid evolutionary algorithms in solving multi-objective optimization problems.

Differential Evolution with Self-adaptation and Local Search for Constrained Multiobjective Optimization

Abstract: This paper presents Differential Evolution with Self-adaptation and Local Search for Constrained Multiobjective Optimization algorithm (DECMOSA-SQP), which uses the self-adaptation mechanism from DEMOwSA algorithm presented at CEC 2007 and a SQP local search. The constrained handling mechanism is also incorporated in the new algorithm. Assessment of the algorithm using CEC 2009 special session and competition on constrained multiobjective optimization test functions is presented. The functions are composed of unconstrained and constrained problems. Their results are assessed using the IGD metric. Based on this metric, algorithm strengths and weaknesses are discussed.

Multiple trajectory search for unconstrained/constrained multi-objective optimization

Abstract: Many real-world optimization problems involve multiple conflicting objectives. Therefore, multi-objective optimization has attracted much attention of researchers and many algorithms have been developed for solving multi-objective optimization problems in the last decade. In this paper the multiple trajectory search (MTS) is presented and successfully applied to thirteen unconstrained and ten constrained multi-objective optimization problems. These problems constitute a test suite provided for competition in the Special Session & Competition on Performance Assessment of Constrained/Bound Constrained Multi-Objective Optimization Algorithms in CEC 2009. In the multiple trajectory search, a set of uniformly distributed solutions is first generated. These solutions will be separated into foreground solutions and background solutions. The search is focuses mainly on foreground solutions and partly on background solutions. The MTS chooses and applies one of the three local search methods on solutions iteratively. The three local search methods begin their search in a very large “neighborhood”. Then the neighborhood contracts step by step until it reaches a pre-defined tiny size, after then, it is reset to its original size. By utilizing such size-varied neighborhood searches, the MTS effectively solves the multi-objective optimization problems.

Guaranteeing Soundness of Configurable Process Variants in Provop

Abstract: Usually, for a particular business process a multitude of variants exist. Each of them constitutes an adjustment of a reference process model to specific requirements building the process context. While some progress has been achieved regarding the configuration of process variants, there exists only little work on how to accomplish this in a sound and efficient manner, especially when considering the large number of process variants that exist in practice as well as the many syntactical and semantical constraints they have to obey. In this paper we discuss advanced concepts for the context- and constraint-based configuration of process variants, and show how they can be utilized to ensure soundness of the configured process variants. Enhancing process-aware information systems with the capability to easily configure sound process models, belonging to the same process family and fitting to the given application context, will enable a new quality in engineering process-aware information systems.

Examination timetabling using late acceptance hyper-heuristics

Abstract: A hyperheuristic is a high level problem solving methodology that performs a search over the space generated by a set of low level heuristics. One of the hyperheuristic frameworks is based on a single point search containing two main stages: heuristic selection and move acceptance. Most of the existing move acceptance methods compare a new solution, generated after applying a heuristic, against a current solution in order to decide whether to reject it or replace the current one. Late Acceptance Strategy is presented as a promising local search methodology based on a novel move acceptance mechanism. This method performs a comparison between the new candidate solution and a previous solution that is generated L steps earlier. In this study, the performance of a set of hyper-heuristics utilising different heuristic selection methods combined with the Late Acceptance Strategy are investigated over an examination timetabling problem. The results illustrate the potential of this approach as a hyperheuristic component. The hyper-heuristic formed by combining a random heuristic selection with Late Acceptance Strategy improves on the best results obtained in a previous study.

Swarm's flight: Accelerating the particles using C-CUDA

Abstract: With the development of Graphics Processing Units (GPU) and the Compute Unified Device Architecture (CUDA) platform, several areas of knowledge are being benefited with the reduction of the computing time. Our goal is to show how optimization algorithms inspired by Swarm Intelligence can take profit from this technology. In this paper, we provide an implementation of the Particle Swarm Optimization (PSO) algorithm in C-CUDA. The algorithm was tested on a suite of well-known benchmark optimization problems and the computing time has been compared with the same algorithm implemented in C and Matlab. Results demonstrate that the computing time can significantly be reduced using C-CUDA. As far as we know, this is the first implementation of PSO in C-CUDA.

WSC-2009: A Quality of Service-Oriented Web Services Challenge

Abstract: With the growing acceptance of service-oriented computing, an emerging area of research is the investigation of technologies that will enable the discovery and composition of web services. The Web Services Challenge (WSC) is a forum where academic and industry researchers can share experiences of developing tools that automate the integration of web services. In the fifth year (i.e. WSC-09) of the Web Services Challenge, software platforms will address several new composition challenges. Requests and results will be transmitted within SOAP messages. Semantics will be represented as ontologies written in OWL, services will be represented in WSDL, and service orchestrations will be represented in WSBPEL. In addition, non-functional properties (Quality of Service) of a service will be represented using WSLA format.

Enhancing MOEA/D with guided mutation and priority update for multi-objective optimization

Abstract: Multi-objective optimization is an essential and challenging topic in the domains of engineering and computation because real-world problems usually include several conflicting objectives. Current trends in the research of solving multi-objective problems (MOPs) require that the adopted optimization method provides an approximation of the Pareto set such that the user can understand the tradeoff between objectives and therefore make the final decision. Recently, an efficient framework, called MOEA/D, combining decomposition techniques in mathematics and optimization methods in evolutionary computation was proposed. MOEA/D decomposes a MOP to a set of single-objective problems (SOPs) with neighborhood relationship and approximates the Pareto set by solving these SOPs. In this paper, we attempt to enhance MOEA/D by proposing two mechanisms. To fully employ the information obtained from neighbors, we introduce a guided mutation operator to replace the differential evolution operator. Moreover, a update mechanism utilizing a priority queue is proposed for performance improvement when the SOPs obtained by decomposition are not uniformly distributed on the Pareto font. Different combinations of these approaches are compared based on the test problem instances proposed for the CEC 2009 competition. The set of problem instances include unconstrained and constrained MOPs with variable linkages. Experimental results are presented in the paper, and observations and discussion are also provided.

A multi-objective approach to Redundancy Allocation Problem in parallel-series systems

Abstract: The Redundancy Allocation Problem (RAP) is a kind of reliability optimization problems. It involves the selection of components with appropriate levels of redundancy or reliability to maximize the system reliability under some predefined constraints. We can formulate the RAP as a combinatorial problem when just considering the redundancy level, while as a continuous problem when considering the reliability level. The RAP employed in this paper is that kind of combinatorial optimization problems. During the past thirty years, there have already been a number of investigations on RAP. However, these investigations often treat RAP as a single objective problem with the only goal to maximize the system reliability (or minimize the designing cost). In this paper, we regard RAP as a multi-objective optimization problem: the reliability of the system and the corresponding designing cost are considered as two different objectives. Consequently, we can utilize a classical Multi-objective Evolutionary Algorithm (MOEA), named Non-dominated Sorting Genetic Algorithm II (NSGA-II), to cope with this multi-objective redundancy allocation problem (MORAP) under a number of constraints. The experimental results demonstrate that the multi-objective evolutionary approach can provide more promising solutions in comparison with two widely used single-objective approaches on two parallel-series systems which are frequently studied in the field of reliability optimization.

Investigation of memory-based multi-objective optimization evolutionary algorithm in dynamic environment

Abstract: As the research of dynamic optimization arising, memory-based strategy has gained public attention recently. However, few studies on developing dynamic multi-objective optimization algorithms and even fewer studies on multi-objective memory-based strategy were reported previously. In this paper, we try to address such an issue by proposing several memory-based multi-objective evolutionary algorithms and experimentally investigating different multi-objective dynamic optimization schemes, which include restart, explicit memory, localsearch memory and hybrid memory schemes. This study is to provide pre-trial research of how to appropriately organize and effectively reuse the changed Pareto-optimal decision values (i.e., Pareto-optimal solutions: POS) information.

Empirical comparison of MOPSO methods - Guide selection and diversity preservation -

Abstract: In this paper, we review several proposals for guide selection in Multi-Objective Particle Swarm Optimization (MOPSO) and compare them with each other in terms of convergence, diversity and computational times. The new proposals made for guide selection, both personal best (‘pbest’) and global best (‘gbest’), are found to be extremely effective and perform well compared to the already existing methods. The combination of selection methods for choosing ‘gbest’ and ‘pbest’ is also studied and it turns out that there exist certain combinations which yield an overall superior performance outperforming the others on the tested benchmark problems. Furthermore, two new proposals namely velocity trigger (as a substitute for “turbulence operator”) and a new scheme of boundary handling is made.

Semantically driven mutation in genetic programming

Abstract: Using semantic analysis, we present a technique known as semantically driven mutation which can explicitly detect and apply behavioural changes caused by the syntactic changes in programs that result from the mutation operation. Using semantically driven mutation, we demonstrate increased performance in genetic programming on seven benchmark genetic programming problems over two different domains.

Performance assessment of Generalized Differential Evolution 3 with a given set of constrained multi-objective test problems

Abstract: This paper presents results for the CEC 2009 Special Session on “Performance Assessment of Constrained / Bound Constrained Multi-Objective Optimization Algorithms” when Generalized Differential Evolution 3 has been used to solve a given set of test problems. The set consist of 23 problems having two, three, or five objectives. Problems have different properties in the sense of separability, modality, and geometry of the Pareto-front. The most of the problems are unconstrained, but 10 problems have one or two constraints. According to the numerical results with an inverted generational distance, Generalized Differential Evolution 3 performed well with all the problems except with one five objective problem. It was noticed that a low crossover control parameter value provides the best average results according to the metric.

Extreme compass and Dynamic Multi-Armed Bandits for Adaptive Operator Selection

Abstract: The goal of Adaptive Operator Selection is the on-line control of the choice of variation operators within Evolutionary Algorithms. The control process is based on two main components, the credit assignment, that defines the reward that will be used to evaluate the quality of an operator after it has been applied, and the operator selection mechanism, that selects one operator based on some operators qualities. Two previously developed Adaptive Operator Selection methods are combined here: Compass evaluates the performance of operators by considering not only the fitness improvements from parent to offspring, but also the way they modify the diversity of the population, and their execution time; Dynamic Multi-Armed Bandit proposes a selection strategy based on the well-known UCB algorithm, achieving a compromise between exploitation and exploration, while nevertheless quickly adapting to changes. Tests with the proposed method, called ExCoDyMAB, are carried out using several hard instances of the Satisfiability problem (SAT). Results show the good synergetic effect of combining both approaches.