GENOCOP: a genetic algorithm for numerical optimization problems with linear constraints
Communications of the ACM - Electronic supplement to the December issue
Multi-Agent Systems: An Introduction to Distributed Artificial Intelligence
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Genetic Algorithms Plus Data Structures Equals Evolution Programs
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Genetic Algorithms in Search, Optimization and Machine Learning
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Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
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Computers and Industrial Engineering - 26th International conference on computers and industrial engineering
Multiobjective Evolutionary Algorithms for solving Constrained Optimization Problems
CIMCA '05 Proceedings of the International Conference on Computational Intelligence for Modelling, Control and Automation and International Conference on Intelligent Agents, Web Technologies and Internet Commerce Vol-2 (CIMCA-IAWTIC'06) - Volume 02
Constraint handling in genetic algorithms using a gradient-based repair method
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Evolutionary algorithms, homomorphous mappings, and constrained parameter optimization
Evolutionary Computation
Evolutionary algorithms for constrained parameter optimization problems
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An evolutionary agent system for mathematical programming
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Semi-elitist evolutionary multi-agent system for multiobjective optimization
ICCS'06 Proceedings of the 6th international conference on Computational Science - Volume Part III
Maintaining diversity in agent-based evolutionary computation
ICCS'06 Proceedings of the 6th international conference on Computational Science - Volume Part III
Stochastic ranking for constrained evolutionary optimization
IEEE Transactions on Evolutionary Computation
Self-adaptive fitness formulation for constrained optimization
IEEE Transactions on Evolutionary Computation
Meta-Lamarckian learning in memetic algorithms
IEEE Transactions on Evolutionary Computation
A multiagent genetic algorithm for global numerical optimization
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A multiagent evolutionary algorithm for constraint satisfaction problems
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Multi-operator based evolutionary algorithms for solving constrained optimization problems
Computers and Operations Research
SEAL'12 Proceedings of the 9th international conference on Simulated Evolution and Learning
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The hurdles in solving Constrained Optimization Problems (COP) arise from the challenge of searching a huge variable space in order to locate feasible points with acceptable solution quality. It becomes even more challenging when the feasible space is very tiny compare to the search space. Usually, the quality of the initial solutions influences the performance of the algorithm in solving such problems. In this paper, we discuss an Evolutionary Agent System (EAS) for solving COPs. In EAS, we treat each individual in the population as an agent. To enhance the performance of EAS for solving COPs with tiny feasible space, we propose a Search Space Reduction Technique (SSRT) as an initial step of our algorithm. SSRT directs the selected infeasible agents in the initial population to move towards the feasible space. The performance of the proposed algorithm is tested on a number of test problems and a real world case problem. The experimental results show that SSRT not only improves the solution quality but also speed up the processing time of the algorithm.