Tackling Real-Coded Genetic Algorithms: Operators and Tools for Behavioural Analysis
Artificial Intelligence Review
An introduction to differential evolution
New ideas in optimization
Handbook of Evolutionary Computation
Handbook of Evolutionary Computation
Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
A computationally efficient evolutionary algorithm for real-parameter optimization
Evolutionary Computation
Introduction to Evolutionary Computing
Introduction to Evolutionary Computing
A Comparison Study of Self-Adaptation in Evolution Strategies and Real-Coded Genetic Algorithms
Evolutionary Computation
Parameter control in evolutionary algorithms
IEEE Transactions on Evolutionary Computation
A robust stochastic genetic algorithm (StGA) for global numerical optimization
IEEE Transactions on Evolutionary Computation
Intelligent evolutionary algorithms for large parameter optimization problems
IEEE Transactions on Evolutionary Computation
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One of the most efficient Real-Coded Genetic Algorithms (RCGAs) for function optimization currently is the G3-PCX (Parent-Centric Crossover) algorithm. However, its performance for solving complex multimodal problems with highly deceptive fitness landscapes is known to be poor compared with its performance for unimodal problems. The problem primarily stems from premature convergence to local rather than global optima due to lack of explorative capabilities of the algorithm. In this study, a hybrid approach combining both adaptive as well as non-adaptive mutation in a cooperative manner is investigated in the hope of improving the explorative capabilities of the G3-PCX algorithm for solving complex multimodal problems. The proposed algorithm is called G3HM (G3-PCX with Hybrid Mutation) and empirical tests on four benchmark complex multimodal test problems have shown highly competitive performance. In two of the four problems, G3HM dramatically outperformed the standard G3-PCX algorithm in terms of solution quality. Thus, the concept of combining adaptive with non-adaptive mutation as a hybrid genetic operator is shown to have beneficial effects for cooperatively exploring complex multimodal search spaces.