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 population-based algorithm-generator for real-parameter optimization
Soft Computing - A Fusion of Foundations, Methodologies and Applications
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This study investigates the augmentation of the crossover-only G3-PCX evolutionary optimization algorithm with mutational diversity for solving multimodal global optimization problems. The objective is to empirically test and compare the optimization precision and dynamics of the two mutationally-augmented G3-PCX algorithms against the original G3-PCX algorithm based on a set of well-known test functions. Empirical tests on five benchmark continuous multimodal test functions have shown highly competitive performance in the augmented G3 algorithms which are called G3MD (G3 with Mutational Diversity) and G3SAM (G3 with Self-Adaptive Mutation) respectively where the proposed algorithms outperformed the standard G3 algorithm in terms of solution quality on two of the test problems. Analysis of the optimization dynamics reveals that the improved performance was due to the longer maintenance of genetic diversity through the mutational augmentation. Hence it is shown that the simple addition of Gaussian mutation can greatly enhance the optimization precision of RCGAs for solving large scale problems with highly deceptive fitness landscapes.