Application of stochastic global optimization algorithms to practical problems
Journal of Optimization Theory and Applications
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Deterministic Global Optimization in Nonlinear Optimal Control Problems
Journal of Global Optimization
A computationally efficient evolutionary algorithm for real-parameter optimization
Evolutionary Computation
An Iterative Global Optimization Algorithm for Potential Energy Minimization
Computational Optimization and Applications
An adaptive penalty formulation for constrained evolutionary optimization
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Ensemble of constraint handling techniques
IEEE Transactions on Evolutionary Computation
Gradual distributed real-coded genetic algorithms
IEEE Transactions on Evolutionary Computation
A method of a spread-spectrum radar polyphase code design
IEEE Journal on Selected Areas in Communications
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Over the last two decades, many different genetic algorithms (GAs) have been introduced for solving optimization problems. Due to the variability of the characteristics in different optimization problems, none of these algorithms has shown consistent performance over a range of real world problems. The success of any GA depends on the design of its search operators, as well as their appropriate integration. In this paper, we propose a GA with a new multi-parent crossover. In addition, we propose a diversity operator to be used instead of mutation and also maintain an archive of good solutions. Although the purpose of the proposed algorithm is to cover a wider range of problems, it may not be the best algorithm for all types of problems. To judge the performance of the algorithm, we have solved aset of constrained optimization benchmark problems, as well as 14 well-known engineering optimization problems. The experimental analysis showed that the algorithm converges quickly to the optimal solution and thus exhibits a superior performance in comparison to other algorithms that also solved those problems.