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Cartesian Ant Programming (CAP) is one of the swarm-based automatic programming method, which combines graph representations in Cartesian Genetic Programming with search mechanism of Ant Colony Optimization. CAP generates a graph structure program based on pheromone sprayed on routes. By using pheromone communication, ants can search the promising solution space intensively. However, once the pheromone...
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...
Surrogate-Assisted Evolutionary Computation (SAEC) has widely applied to approximate an objective function. However, SAEC may potentially also reduce the processing time of inexpensive optimization problems wherein solutions are evaluated within a few seconds or minutes. To achieve this, the approximation model of a fitness function should be iterated as few times as possible during optimization....
The type of evolutionary machine learning known as grammatical Evolution (GE) is currently receiving a great deal of attention. GE is particularly suitable for developing decision-tree classifiers because of a framework, in which candidate solutions are generated via production rules. Various decision-tree classifier methods based on GE have been proposed. In general, the performance of GE systems...
In recent years, Cartesian Ant Programming (CAP) has been proposed as a swarm-based automatic programming method, which combines graph representations in Cartesian Genetic Programming with search mechanism of Ant Colony Optimization. In CAP, once an ant jumps a number of nodes, the skipped nodes are not utilized and wasted in search. To make the use frequency of nodes uniform, we propose CAP with...
The paper presents particle swarm optimization (PSO) with dynamic search strategies based on landscape modality estimation. In order to control search strategies, we introduce landscape modality estimation method using correlation coefficients between rankings of search points to PSO. This estimation method utilizes relationship between fitness and distance to a reference point to classify whether...
In this paper, we focus on evolutionary optimization of multi-agent behavior. In our previous work, we have proposed a multi-agent control model based on Cartesian Genetic Programming (CGP). In CGP, each individual is represented by a graph-structural program. The CGP has a characteristics that each individual has multiple output nodes. Therefore, by assigning the outputs to respective agents, we...
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...
In this paper, we focus on solving symbolic regression problems, in which we find functions approximating the relationships between given input and output data. 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 for efficient search. In the semantics-based...
In this paper, we focus on evolutionary optimization of multi-agent behavior. There are two representative models for multi-agent control, homogeneous and heterogeneous models. In the homogeneous model, all agents are controlled by the same controller. Therefore, it is difficult to realize complex cooperative behavior such as division of labors. In contrast, in the heterogeneous model, respective...
Genetic Programming (GP) is one of the evolutionary algorithm that automatically creates a computer program. Cartesian GP (CGP) is one of the extensions of GP, which generates the graph structural programs. By using the graph structure, the solutions can be represented by more compact programs. Therefore, CGP is widely applied to the various problems. As a different approach from the evolutionary...
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)...
In this paper, we focus on symbolic regression problems, in which we find functions approximating the relationships between given input and output data. If we do not have the knowledge on the structure (e.g. Degree) of the true functions, Genetic Programming (GP) is often used for evolving tree structural numerical expressions. In GP, crossover operator has a great influence on the quality of the...
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, one of the evolutionary algorithms, is a population-based stochastic search technique for solving optimization problems in a continuous space. Due to its simplicity, effectiveness and robustness, DE has been applied to a variety of real-world problems. As one of the successful application, DE is incorporated to interactive evolutionary computation (IEC) framework. Interactive...
Ant Colony Optimization (ACO) is a swarm-based search method. Multiple ant agents search various solutions and their searches focus on around good solutions by positive feedback mechanism based on pheromone communication. ACO is effective for combinatorial optimization problems. The attempt of applying ACO to automatic programming has been studied in recent years. As one of the attempts, we have previously...
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...
Subtree exchange crossover which is usually used in Genetic Programming (GP) can not control the search properties such as global or local search, because crossover points in parental individuals are selected at random. To overcome the problem, crossover based on semantic distance of subtrees has been studied recent years. If similar subtrees in semantic space are exchanged, the local search can be...
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...
Neuroevolution has been widely used for action control of agents. Agent controllers are represented by Neural Networks (NN), and the connection weights and/or the structure of NN are optimized by evolutionary computation such as Particle Swarm Optimization (PSO). The agent's perceptual inputs are used as the inputs of NN. When the framework is applied to the agent control in platform games where a...
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