Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic algorithms + data structures = evolution programs (3rd ed.)
Orgy in the Computer: Multi-Parent Reproduction in Genetic Algorithms
Proceedings of the Third European Conference on Advances in Artificial Life
Genetic AlgorithmsNumerical Optimizationand Constraints
Proceedings of the 6th International Conference on Genetic Algorithms
A new adaptive penalty scheme for genetic algorithms
Information Sciences: an International Journal - Special issue: Evolutionary computation
Evolutionary algorithms, homomorphous mappings, and constrained parameter optimization
Evolutionary Computation
Stochastic ranking for constrained evolutionary optimization
IEEE Transactions on Evolutionary Computation
A simple multimembered evolution strategy to solve constrained optimization problems
IEEE Transactions on Evolutionary Computation
Task Decomposition for Optimization Problem Solving
SEAL '08 Proceedings of the 7th International Conference on Simulated Evolution and Learning
An agent-based memetic algorithm (AMA) for nonlinear optimization with equality constraints
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Classification-Assisted Memetic Algorithms for Equality-Constrained Optimization Problems
AI '09 Proceedings of the 22nd Australasian Joint Conference on Advances in Artificial Intelligence
An evolutionary agent system for mathematical programming
ISICA'07 Proceedings of the 2nd international conference on Advances in computation and intelligence
Feasibility structure modeling: an effective chaperone for constrained memetic algorithms
IEEE Transactions on Evolutionary Computation - Special issue on preference-based multiobjective evolutionary algorithms
Localized genetic algorithm for vehicle routing problem with time windows
Applied Soft Computing
Constrained optimization based on modified differential evolution algorithm
Information Sciences: an International Journal
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Many optimization problems that involve practical applications have functional constraints, and some of these constraints are active, meaning that they prevent any solution from improving the objective function value beyond the constraint limits. Therefore, the optimal solution usually lies on the boundary of the feasible region. In order to converge faster when solving such problems, a new ranking and selection scheme is introduced which exploits this feature of constrained problems. In conjunction with selection, a new crossover method is also presented based on three parents. When comparing the results of this new algorithm with four other evolutionary based methods, using nine benchmark problems from the relevant literature, it shows very encouraging performance.