Use of a self-adaptive penalty approach for engineering optimization problems
Computers in Industry
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Theory and Practice of Uncertain Programming
Theory and Practice of Uncertain Programming
Constraint Handling in Genetic Algorithms: The Set Partitioning Problem
Journal of Heuristics
A Multi-objective Approach to Constrained Optimisation of Gas Supply Networks: the COMOGA Method
Selected Papers from AISB Workshop on Evolutionary Computing
Evolutionary algorithms, homomorphous mappings, and constrained parameter optimization
Evolutionary Computation
Evolutionary algorithms for constrained parameter optimization problems
Evolutionary Computation
Evolutionary programming techniques for constrained optimizationproblems
IEEE Transactions on Evolutionary Computation
Stochastic ranking for constrained evolutionary optimization
IEEE Transactions on Evolutionary Computation
Self-adaptive fitness formulation for constrained optimization
IEEE Transactions on Evolutionary Computation
A hybrid genetic algorithm and bacterial foraging approach for global optimization
Information Sciences: an International Journal
Search space reduction technique for constrained optimization with tiny feasible space
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Expert Systems with Applications: An International Journal
Multi-objective genetic local search algorithm using Kohonen's neural map
Computers and Industrial Engineering
A flexible tolerance genetic algorithm for optimal problems with nonlinear equality constraints
Advanced Engineering Informatics
Infeasibility handling in genetic algorithm using nested domains for production planning
Computers and Operations Research
A novel hybrid constraint handling technique for evolutionary optimization
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
Synergy of evolutionary algorithm and socio-political process for global optimization
Expert Systems with Applications: An International Journal
An evolutionary agent system for mathematical programming
ISICA'07 Proceedings of the 2nd international conference on Advances in computation and intelligence
Expert Systems with Applications: An International Journal
International Journal of Bio-Inspired Computation
Feasibility structure modeling: an effective chaperone for constrained memetic algorithms
IEEE Transactions on Evolutionary Computation - Special issue on preference-based multiobjective evolutionary algorithms
Computers and Operations Research
Multi-operator based evolutionary algorithms for solving constrained optimization problems
Computers and Operations Research
Computers and Industrial Engineering
Inequality constraint handling in genetic algorithms using a boundary simulation method
Computers and Operations Research
Modified harmony search optimization for constrained design problems
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Constrained optimization based on modified differential evolution algorithm
Information Sciences: an International Journal
An artificial fish swarm filter-based method for constrained global optimization
ICCSA'12 Proceedings of the 12th international conference on Computational Science and Its Applications - Volume Part III
Empirical evaluation of search based requirements interaction management
Information and Software Technology
Fuzzy multi-period portfolio selection optimization models using multiple criteria
Automatica (Journal of IFAC)
A particle swarm-BFGS algorithm for nonlinear programming problems
Computers and Operations Research
Computers and Electronics in Agriculture
| Hi-index | 0.02 |
Constraint handling is one of the major concerns when applying genetic algorithms (GAs) to solve constrained optimization problems. This paper proposes to use the gradient information derived from the constraint set to systematically repair infeasible solutions. The proposed repair procedure is embedded into a simple GA as a special operator. Experiments using 11 benchmark problems are presented and compared with the best known solutions reported in the literature. Our results are competitive, if not better, compared to the results reported using the homomorphous mapping method, the stochastic ranking method, and the self-adaptive fitness formulation method.