Efficient genetic algorithms for solving hard constrained optimization problems

Efficient Genetic Algorithms for Solving Hard Constrained Optimization Problems

Using Genetic Algorithms for Solving Hard Problems in GIS

OpenMP Dual Population Genetic Algorithm for Solving Constrained Optimization Problems

Dual Population Genetic Algorithm is an effective optimization algorithm that provides additional diversity to the main population. It deals with the premature convergence problem as well as the diversity problem associated with Genetic Algorithm. But dual population introduces additional search space that increases time required to find an optimal solution. This large scale search space proble...

Dual Population Genetic Algorithm for Solving Constrained Optimization Problems

Dual Population Genetic Algorithm is an effective optimization algorithm that provides additional diversity to the main population. It addresses the premature convergence problem as well as the diversity problem associated with Genetic Algorithm. Thus it restricts their individuals to be trapped in the local optima. This paper proposes Dual Population Genetic Algorithm for solving Constrained O...

Solving Constrained Optimization Problems with Hybrid Evolutionary Algorithms

The foundations for evolutionary algorithms (EAs) were established in the end of the 60’s [1, 2] (EAs) and strengthened in the beginning of the 70’s [3, 4]. EAs appeared as an alternative to the exact or approximate optimization methods whose application to many real problems were not acceptable in terms of performance. When applied to real problems, EAs provide a valuable relation between qual...