• International Journal of Control, Automation, and Systems
• /
• v.2 no.4
• /
• pp.463-474
• /
• 2004

Game theory is a method of mathematical analysis developed to study the decision making process. In 1928, Von Neumann mathematically proved that every two-person, zero-sum game with many pure finite strategies for each player is deterministic. In the early 50's, Nash presented another concept as the basis for a generalization of Von Neumann's theorem. Another central achievement of game theory is the introduction of evolutionary game theory, by which agents can play optimal strategies in the absence of rationality. Through the process of Darwinian selection, a population of agents can evolve to an Evolutionary Stable Strategy (ESS) as introduced by Maynard Smith in 1982. Keeping pace with these game theoretical studies, the first computer simulation of coevolution was tried out by Hillis. Moreover, Kauffman proposed the NK model to analyze coevolutionary dynamics between different species. He showed how coevolutionary phenomenon reaches static states and that these states are either Nash equilibrium or ESS in game theory. Since studies concerning coevolutionary phenomenon were initiated, there have been numerous other researchers who have developed coevolutionary algorithms. In this paper we propose a new coevolutionary algorithm named Game theory based Coevolutionary Algorithm (GCEA) and we confirm that this algorithm can be a solution of evolutionary problems by searching the ESS. To evaluate this newly designed approach, we solve several test Multiobjective Optimization Problems (MOPs). From the results of these evaluations, we confirm that evolutionary game can be embodied by the coevolutionary algorithm and analyze the optimization performance of our algorithm by comparing the performance of our algorithm with that of other evolutionary optimization algorithms.

• Korean Management Science Review
• /
• v.27 no.3
• /
• pp.15-31
• /
• 2010

We consider a reverse logistics network design problem for recycling. The problem consists of three stages of transportation. In the first stage products are transported from retrieval centers to disassembly centers. In the second stage disassembled modules are transported from disassembly centers to processing centers. Finally, in the third stage modules are transported from either processing centers or a supplier to a manufacturer, a recycling site, or a disposal site. The objective is to design a network which minimizes the total transportation cost. We design a cooperative coevolutionary algorithm to solve the problem. First, the problem is decomposed into three subproblems each of which corresponds to a stage of transportation. For subproblems 1 and 2, a population of chromosomes is constructed. Each chromosome in the population is coded as a permutation of integers and an algorithm which decodes a chromosome is suggested. For subproblem 3, an heuristic algorithm is utilized. Then, a performance evaluation procedure is suggested which combines the chromosomes from each of two populations and the heuristic algorithm for subproblem 3. An experiment was carried out using test problems. The experiments showed that the cooperative coevolutionary algorithm generally tends to show better performances than the previous genetic algorithm as the problem size gets larger.

• International Journal of Control, Automation, and Systems
• /
• v.5 no.4
• /
• pp.444-455
• /
• 2007

An approach to construct multiple interpretable and precise fuzzy systems based on the Pareto Multi-objective Cooperative Coevolutionary Algorithm (PMOCCA) is proposed in this paper. First, a modified fuzzy clustering algorithm is used to construct antecedents of fuzzy system, and consequents are identified separately to reduce computational burden. Then, the PMOCCA and the interpretability-driven simplification techniques are executed to optimize the initial fuzzy system with three objectives: the precision performance, the number of fuzzy rules and the number of fuzzy sets; thus both the precision and the interpretability of the fuzzy systems are improved. In order to select the best individuals from each species, we generalize the NSGA-II algorithm from one species to multi-species, and propose a new non-dominated sorting technique and collaboration mechanism for cooperative coevolutionary algorithm. Finally, the proposed approach is applied to two benchmark problems, and the results show its validity.

• Journal of Korean Institute of Industrial Engineers
• /
• v.26 no.2
• /
• pp.101-109
• /
• 2000

A competitive coevolutionary algorithm is a probabilistic search method that imitates the biological process that two or more species competitively coevolve through evolutionary arms race. The algorithm has been used to efficiently solve adversarial problems that can be formulated as the search for a solution that is correct over a large space of test cases. We develop an efficient competitive coevolutionary algorithm to solve adversarial problems with high complexity. The algorithm developed in this paper employs three methods: tournament competitions, exchanging of entry fee, and localized coevolution. Analyzed in this paper are the effects of the methods on the performance of the proposed algorithm. The extensive experiments show that our algorithm can progress an evolutionary arms race between competitive coevolving species and then outperforms existing approaches to solving the adversarial problems.

• Korean Management Science Review
• /
• v.29 no.2
• /
• pp.21-34
• /
• 2012

This paper addresses a network design problem in a supply chain system that involves locating both plants and distribution centers, and determining the best strategy for distributing products from the suppliers to the plants, from the plants to the distribution centers and from the distribution centers to the customers. This paper suggests a cooperative coevolutionary algorithm (CCEA) approach to solve the model. First, the problem is decomposed into three subproblems for each of which the chromosome population is created correspondingly. Each chromosome in each population is represented as a permutation denoting the priority. Then an algorithm generating a solution from the combined set of chromosomes from each population is suggested. Also an algorithm evaluating the performance of a solution is suggested. An experimental study is carried out. The results show that our CCEA tends to generate better solutions than the previous CCEA as the problem size gets larger and that the permutation representation for chromosome used here is better than other representation.

• Journal of the Korean Operations Research and Management Science Society
• /
• v.23 no.4
• /
• pp.187-201
• /
• 1998

In ATM networks with high capacity, the effect of failures on transmission links or nodes can be catastrophic, so that the issue of survivability is of great importance. In this paper. we consider the routing problem for working and backup virtual paths(VPs). To accomplish a higher survivability. routing the two kinds of VPs should be taken into account at the same time because backup VP routing depends on the working VP routing. A coevolutionary algorithm is employed to solve the problem for simultaneously routing of working and backup VPs. To develop an efficient coevolutionary algorithm for the problem. structure of populations, encoding method, neighborhood, and genetic operators are studied in this paper. The results of extensive experiments are reported. The performance comparison of the proposed algorithm with a conventional genetic algorithm and existing heuristics shows that our approach is promising.

• Journal of Korean Institute of Industrial Engineers
• /
• v.28 no.1
• /
• pp.87-98
• /
• 2002

A competitive coevolutionary algorithm is a probabilistic search method that imitates coevolution process through evolutionary arms race. The algorithm has been used to solve adversarial problems. In the algorithms, the selection of competitors is needed to evaluate the fitness of an individual. The goal of this study is to compare and analyze several competition strategies in terms of solution quality, convergence speed, balance between competitive coevolving species, population diversity, etc. With two types of test-bed problems, game problems and solution-test problems, extensive experiments are carried out. In the game problems, sampling strategies based on fitness have a risk of providing bad solutions due to evolutionary unbalance between species. On the other hand, in the solution-test problems, evolutionary unbalance does not appear in any strategies and the strategies using information about competition results are efficient in solution quality. The experimental results indicate that the tournament competition can progress an evolutionary arms race and then is successful from the viewpoint of evolutionary computation.

• Journal of Korean Society for Quality Management
• /
• v.39 no.1
• /
• pp.98-108
• /
• 2011

Clustering is an useful method to classify objects into subsets that have some meaning in the context of a particular problem and has been applied in variety of fields, customer relationship management, data mining, pattern recognition, and biotechnology etc. This paper addresses the unknown K clustering problems and presents a new approach based on a coevolutionary algorithm to solve it. Coevolutionary algorithms are known as very efficient tools to solve the integrated optimization problems with high degree of complexity compared to classical ones. The problem considered in this paper can be divided into two sub-problems; finding the number of clusters and classifying the data into these clusters. To apply to coevolutionary algorithm, the framework of algorithm and genetic elements suitable for the sub-problems are proposed. Also, a neighborhood-based evolutionary strategy is employed to maintain the population diversity. To analyze the proposed algorithm, the experiments are performed with various test-bed problems which are grouped into several classes. The experimental results confirm the effectiveness of the proposed algorithm.

• Proceedings of the Korean Operations and Management Science Society Conference
• /
• 2003.05a
• /
• pp.1071-1076
• /
• 2003

Evolutionary algorithm is recognized as a promising approach to solving multi-objective combinatorial optimization problems. When no preference information of decision makers is given, multi-objective optimization problems have been commonly used to search for diverse and good Pareto optimal solution. In this paper we propose a new multi-objective evolutionary algorithm based on competitive coevolutionary algorithm, and demonstrate the applicability of the algorithm. The proposed algorithm is designed to promote both population diversity and rapidity of convergence. To achieve this, the strategies of fitness evaluation and the operation of the Pareto set are developed. The algorithm is applied to job shop scheduling problems (JSPs). The JSPs have two objectives: minimizing makespan and minimizing earliness or tardiness. The proposed algorithm is compared with existing evolutionary algorithms in terms of solution quality and diversity. The experimental results reveal the effectiveness of our approach.

• Proceedings of the Korean Operations and Management Science Society Conference
• /
• 2003.05a
• /
• pp.1048-1055
• /
• 2003

This paper proposes a new method of solving integrated problems that consist of several sub-problems in a symmetric multi-layered structure, and also demonstrate the applicability of the method. The proposed method is named Symmetric Multi-layered Coevolutionary Algorithm (SMCA). The SMCA imitates partly the natural process of endosymbiotic evolution, which is a special type of coevolution. The SMCA is applied to the process planning problem in flexible manufacturing system (FMS), taking account of the flexibility of machine, tool, process, and sequence. To do this, SMCA's components are studied and its strategies are developed to improve the performance. The proposed algorithm is compared with the existing ones in terms of solution quality. The experimental results confirm the effectiveness of our approach.