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To solve symbolic regression problems, Genetic Programming (GP) is often used for evolving tree structural numerical expressions. Recently, new crossover operators based on semantics of tree structures have attracted many attentions. In the semantics-based crossover, offspring is created from its parental individuals so that the offspring can inherit the characteristics of the parents not structurally...
The paper presents adaptive particle swarm optimization with multi-dimensional mutation (MM-APSO), which can perform move efficient search than the conventional adaptive particle swarm optimization (APSO). In particular, it can solve non-separable fitness functions such as banana functions with high accuracy and rapid convergence. MM-APSO consists of APSO and additional two methods. One is multi-dimensional...
Genetic Programming (GP) is an evolutionary optimization method for generating tree structural programs. It is important to maintain the population diversity for preventing GP search from falling into local optima. For this purpose, we propose a new method which introduces a concept of genealogy into the population. We call the method Genetic Programming using the Best Individuals of Genealogies (GPBIG)...
Differential evolution (DE) is a simple yet efficient evolutionary algorithm. Because of its simplicity, effectiveness and robustness, DE has gradually become more popular and applied in various fields. In addition, a lot of works have been done to improve the search ability of DE. Among them, opposition-based DE (ODE), which is incorporated opposition-based learning (OBL), has shown better performance...
Genetic Programming (GP) and Simulated Annealing Programming (SAP) are typical metaheuristic methods for automatic programming. We propose a new method, Parallel — Genetic and Annealing Programming (P-GAP) which combines GP and SAP. In P-GAP, multiple initial populations are generated by SAP. Respective populations evolve by parallel GP. As the generation proceeds, populations are integrated gradually...
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