Constraint satisfaction on dynamic environments by the means of coevolutionary genetic algorithms

We discuss adaptability of evolutionary computations in dynamic environments. We introduce two classes of dynamic environments which are utilizing the notion of constraint satisfaction problems: changeover and gradation. The changeover environment is a problem class which consists of a sequence of the constraint networks with the same nature. On the other hand, the gradation environment is a problem class which consists of a sequence of the constraint networks such that the sequence is associated with two constraint networks, i. e., initial and target, and all constraint networks in the sequence metamorphosis from the initial constraint network to the target constraint network. We compare coevolutionary genetic algorithms with SGA in computational simulations. Experimental results on the above dynamic environments confirm us the effectiveness of our approach, i.e., coevolutionary genetic algorithm</p

Estimation of Bayesian network algorithm with GA searching for better network structure

Estimation of Bayesian network algorithms, which adopt Bayesian networks as the probabilistic model were one of the most sophisticated algorithms in the estimation of distribution algorithms. However the estimation of Bayesian network is key topic of this algorithm, conventional EBNAs adopt greedy searches to search for better network structures. In this paper, we propose a new EBNA, which adopts genetic algorithm to search the structure of Bayesian network. In order to reduce the computational complexity of estimating better network structures, we elaborates the fitness function of the GA module, based upon the synchronicity of specific pattern in the selected individuals. Several computational simulations on multidimensional knapsack problems show us the effectiveness of the proposed method.</p

A new fitness function for discovering a lot of satisfiable solutions in constraint satisfaction problems

In this paper, we discuss how many satisfiable solutions a genetic algorithm can find in a problem instance of a constraint satisfaction problems in a single execution. Hence, we propose a framework for a new fitness function which can be applied to traditional fitness functions. However, the mechanism of the proposed fitness function is quite simple, and several experimental results on a variety of instances of general constraint satisfaction problems demonstrate the effectiveness of the proposed fitness function</p

Perception-action rule acquisition by coevolutionary fuzzy classifier system

Recently, many researchers have studied the techniques in applying a fuzzy classifier system (FCS) to control mobile robots, since the FCS can easily treat continuous inputs, such as sensors and images by using a fuzzy number. By using the FCS, however, only reflective rules are acquired. Thus, in the proposed approach, an additional genetic algorithm is incorporated in order to search for strategic knowledge, i.e., the sequence of effective activated rules in the FCS. Therefore, the proposed method consists of two modules: an ordinal FCS and the genetic algorithm. Computational experiments based on WEBOTS, one of the Khepera robot simulators, confirm the effectiveness of the proposed method</p

Coevolutionary genetic algorithm for constraint satisfaction with a genetic repair operator for effective schemata formation

We discuss a coevolutionary genetic algorithm for constraint satisfaction. Our basic idea is to explore effective genetic information in the population, i.e., schemata, and to exploit the genetic information in order to guide the population to better solutions. Our coevolutionary genetic algorithm (CGA) consists of two GA populations; the first GA, called “H-GA”, searches for the solutions in a given environment (problem), and the second GA, called “P-GA”, searches for effective genetic information involved in the H-GA, namely, good schemata. Thus, each individual in P-GA consists of alleles in H-GA or “don't care” symbol representing a schema in the H-GA. These GA populations separately evolve in each genetic space at different abstraction levels and affect with each other by two genetic operators: “superposition” and “transcription”. We then applied our CGA to constraint satisfaction problems (CSPs) incorporating a new stochastic “repair” operator for P-GA to raise the consistency of schemata with the (local) constraint conditions in CSPs. We carried out two experiments: First, we examined the performance of CGA on various “general” CSPs that are generated randomly for a wide variety of “density” and “tightness” of constraint conditions in the CSPs that are the basic measures of characterizing CSPs. Next, we examined “structured” CSPs involving latent “cluster” structures among the variables in the CSPs. For these experiments, computer simulations confirmed us the effectiveness of our CGA</p

Coevolutionary GA with schema extraction by machine learning techniques and its application to knapsack problems

The authors introduce a novel coevolutionary genetic algorithm with schema extraction by machine learning techniques. Our CGA consists of two GA populations: the first GA (H-GA) searches for the solutions in the given problems and the second GA (P-GA) searches for effective schemata of the H-GA. We aim to improve the search ability of our CGA by extracting more efficiently useful schemata from the H-GA population, and then incorporating those extracted schemata in a natural manner into the P-GA. Several computational simulations on multidimensional knapsack problems confirm the effectiveness of the proposed method</p

Mixed Reality Human Media for Social and Physical Interaction

Abstract: This paper outlines new facilities within ubiquitous human media spaces supporting embodied interaction between humans and computation both socially and physically. We believe that the current approach to developing electronic based design environments is lacking with regard to support for multi-person multi-modal design interactions. In this paper, we present an alternative ubiquitous computing environment based on an integrated design of real and virtual worlds. We implement three different research prototype systems: the Virtual Kyoto Garden, Touchy Internet, and the Human Pacman. The functional capabilities implemented in these systems include spatially-aware 3D navigation, tangible interaction, and ubiquitous human media spaces. Some of its details, benefits, and issues regarding design support are discussed

スウリョウカホウ ニ ヨル シュウダン コウゾウ ト シュウダン ゾクセイ ノ シュウヤクカ ニ カンスル ケンキュウ

京都大学0048新制・論文博士工学博士乙第3938号論工博第1197号新制||工||448(附属図書館)UT51-54-P349(主査)教授 岩井 壮介, 教授 明石 一, 教授 椹木 義一学位規則第5条第2項該当Kyoto UniversityDFA

Estimation of Bayesian network algorithm with GA searching for better network structure

Estimation of Bayesian network algorithms, which adopt Bayesian networks as the probabilistic model were one of the most sophisticated algorithms in the estimation of distribution algorithms. However the estimation of Bayesian network is key topic of this algorithm, conventional EBNAs adopt greedy searches to search for better network structures. In this paper, we propose a new EBNA, which adopts genetic algorithm to search the structure of Bayesian network. In order to reduce the computational complexity of estimating better network structures, we elaborates the fitness function of the GA module, based upon the synchronicity of specific pattern in the selected individuals. Several computational simulations on multidimensional knapsack problems show us the effectiveness of the proposed method.</p