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In recent years, semantics-based crossover operators have attracted attention for efficient search in Genetic Programming (GP). Geometric Semantic Genetic Programming (GSGP) is one of the methods, in which a convex combination of two parents is used for creating an offspring. We have previously proposed an improved GSGP, Deterministic GSGP. In Deterministic GSGP, the convex combination is relaxed...
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 one of the most powerful global numerical optimization algorithms in the field of evolutionary algorithm. However, the performance of DE is affected by control parameters and mutation strategies. In addition, the choice of the control parameters and mutation strategies is strongly dependent on the characteristics of optimization problems. As a result, studies focused...
Differential Evolution (DE) is one of the evolutionary algorithm that was developed to handle optimization problems over continuous domains. It's a population-based stochastic search technique with simple concept and high efficient. In recent year, many DE variants were proposed and have been applied for solving various problems. In addition, some DE based techniques are modified to handle discrete...
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...
Differential evolution (DE) is one of the evolutionally algorithms for solving optimization problems in a continuous space. DE has been widely applied to solve various optimization problems. Additionally, many modified DE algorithms have been developed in an attempt to improve search performance. In this paper, we propose island-based DE with varying subpopulation size. Island model is one of the...
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...
Genetic Programming (GP) is an evolutionary method for generating tree structural programs. Normal subtree crossover in GP randomly selects a crossover point in each parental tree, and offspring are created by exchanging the selected subtrees. In the normal crossover, it is difficult to control the global and local search because the similarity between the subtrees is not considered. In this paper,...
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