Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic algorithms + data structures = evolution programs (3rd ed.)
An introduction to differential evolution
New ideas in optimization
Mechanical engineering design optimization by differential evolution
New ideas in optimization
Evolution and Optimum Seeking: The Sixth Generation
Evolution and Optimum Seeking: The Sixth Generation
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Evolutionary Algorithms for Solving Multi-Objective Problems
Evolutionary Algorithms for Solving Multi-Objective Problems
Multiobjective-Based Concepts to Handle Constraints in Evolutionary Algorithms
ENC '03 Proceedings of the 4th Mexican International Conference on Computer Science
A constraint handling approach for the differential evolution algorithm
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
Evolutionary algorithms, homomorphous mappings, and constrained parameter optimization
Evolutionary Computation
Evolutionary algorithms for constrained parameter optimization problems
Evolutionary Computation
Search biases in constrained evolutionary optimization
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
System design by constraint adaptation and differential evolution
IEEE Transactions on Evolutionary Computation
Stochastic ranking for constrained evolutionary optimization
IEEE Transactions on Evolutionary Computation
Saving Evaluations in Differential Evolution for Constrained Optimization
ENC '05 Proceedings of the Sixth Mexican International Conference on Computer Science
Information Sciences: an International Journal
Differential evolution with dynamic stochastic selection for constrained optimization
Information Sciences: an International Journal
Parameter control in differential evolution for constrained optimization
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Differential evolution with level comparison for constrained optimization
ICIC'09 Proceedings of the Intelligent computing 5th international conference on Emerging intelligent computing technology and applications
Differential evolution in constrained numerical optimization: An empirical study
Information Sciences: an International Journal
A new self-adaption differential evolution algorithm based component model
ISICA'10 Proceedings of the 5th international conference on Advances in computation and intelligence
SEAL'06 Proceedings of the 6th international conference on Simulated Evolution And Learning
Constrained optimization based on modified differential evolution algorithm
Information Sciences: an International Journal
An improved (µ+λ)-constrained differential evolution for constrained optimization
Information Sciences: an International Journal
SEAL'12 Proceedings of the 9th international conference on Simulated Evolution and Learning
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In this paper, we incorporate a diversity mechanism to the differential evolution algorithm to solve constrained optimization problems without using a penalty function. The aim is twofold: (1) to allow infeasible solutions with a promising value of the objective function to remain in the population and also (2) to increase the probabilities of an individual to generate a better offspring while promoting collaboration of all the population to generate better solutions. These goals are achieved by allowing each parent to generate more than one offspring. The best offspring is selected using a comparison mechanism based on feasibility and this child is compared against its parent. To maintain diversity, the proposed approach uses a mechanism successfully adopted with other evolutionary algorithms where, based on a parameter Sr a solution (between the best offspring and the current parent) with a better value of the objective function can remain in the population, regardless of its feasibility. The proposed approach is validated using test functions from a well-known benchmark commonly adopted to validate constraint-handling techniques used with evolutionary algorithms. The statistical results obtained by the proposed approach are highly competitive (based on quality, robustness and number of evaluations of the objective function) with respect to other constraint-handling techniques, either based on differential evolution or on other evolutionary algorithms, that are representative of the state-of-the-art in the area. Finally, a small set of experiments were made to detect sensitivity of the approach to its parameters.