Naoki Mori

Adaption to a Changing Environment by Means of the Thermodynamical Genetic Algorithm

In applications of the genetic algorithms (GA) to problems of adaptation to changing environments, maintenance of the diversity of the population is an essential requirement. Taking this point into consideration, the authors have proposed to utilize the thermodynamical genetic algorithm (TDGA) for the problems of adaptation to changing environments. The TDGA is a genetic algorithm that uses a selection rule inspired by the principle of the minimal free energy in thermodynamical systems. In the present paper, the authors propose a control method of the temperature, an adjustable parameter in the TDGA. The temperature is controlled by a feedback technique so as to regulate the level of the diversity of the population measured by entropy. The adaptation ability of the proposed method is confirmed by computer simulation taking time-varying knapsack problems as examples.

Using compression to understand the distribution of building blocks in genetic programming populations

Compression algorithms generate a predictive model of data, using the model to reduce the number of bits required to transmit the data (in effect, transmitting only the differences from the model). As a consequence, the degree of compression achieved provides an estimate of the level of regularity in the data. Previous work has investigated the use of these estimates to understand the replication of building blocks within genetic programming (GP) individuals, and hence to understand how different GP algorithms promote the evolution of repeated common structure within individuals. Here, we extend this work to the population level, and use it to understand the extent of similarity between sub-structures within individuals in GP populations.

Analysing the regularity of genomes using compression and expression simplification

We propose expression simplification and tree compression as aids in understanding the evolution of regular structure in Genetic Programming individuals.We apply the analysis to two previously-published algorithms, which aimed to promote regular and repeated structure. One relies on subtree duplication operators, the other uses repeated evaluation during a developmental process. Both successfully generated solutions to difficult problems, their success being ascribed to promotion of regular structure. Our analysis modifies this ascription: the evolution of regular structure is more complex than anticipated, and the success of the techniques may have arisen from a combination of promotion of regularity, and other, so far unidentified, effects.

Adaptation to a dynamical environment by means of the environment identifying genetic algorithm

Adaptation to dynamic environments is an important application of genetic algorithms (GAs). However, there are many difficulties to apply the GA to dynamic environments. Especially, in online environments, the GAs defects become remarkable because individuals should be evaluated in the real world. We proposes a novel approach to such an online adaptation called the environment identifying genetic algorithm (EIGA). Computer simulation is carried out by taking an Nk-landscape problem as an example.

Estimating the distribution and propagation of genetic programming building blocks through tree compression

Shin et al [19] and McKay et al [15] previously applied tree compression and semantics-based simplification to study the distribution of building blocks in evolving Genetic Programming populations. However their method could only give static estimates of the degree of repetition of building blocks in one generation at a time, supplying no information about the flow of building blocks between generations. Here, we use a state-of-the-art tree compression algorithm, xmlppm, to estimate the extent to which frequent building blocks from one generation are still in use in a later generation.n While they compared the behaviour of different GP algorithms on one specific problem -- a simple symbolic regression problem -- we extend the analysis to a more complex problem, a symbolic regression problem to find a Fourier approximation to a sawtooth wave, and to a Boolean domain, odd parity.

A communication network model of electric power trading systems using Web services

Many business models of power trading systems have been proposed to aim at load reduction by consumers cooperating with electric power suppliers in an electric power market. On the other hand, Web services are regarded as a new application paradigm in the world of the Internet. Then, we propose a network model of power trading systems using Web services in this paper. The adaptability of Web services to power trading systems was checked in the prototype of our network model and we got good results in our simulations. Each server provides functions as a SOAP (simple object access protocol) server, and it is coupled loosely with each other through SOAP. Storing SOAP message in HTTP packet can establish a penetration communication way that is not conscious of a firewall. Switching of dynamic servers is possible by means of rewriting the server point information.

2-Scene Comic Creating System Based on the Distribution of Picture State Transition

Understanding picture by computer has become one of the most important topics in computer science. However, there are few researches have been reported about human like picture understanding by computer. The main reason of difficulty is that lots of picture expressions contain more lyric aspect than natural language expressions. Comic is one of the best target of picture understanding researches because pictures in comics express simply and clearly story, therefore we can presume that pictures in comics have strong universality. Picture understanding is defined as understanding situations and estimating transition between current scene and next scene. In this paper, The novel method which generates pictures using prepared picture parts and image objects databases is proposed. We also show the 2-scene comics creating system using user inputs picture and propose the representation of picture state transition.

A New Method for Simplifying Algebraic Expressions in Genetic Programming Called Equivalent Decision Simplification

Symbolic Regression is one of the most important applications of Genetic Programming, but these applications suffer from one of the key issues in Genetic Programming, namely bloat – the uncontrolled growth of ineffective code segments, which do not contribute to the value of the function evolved, but complicate the evolutionary proces, and at minimum greatly increase the cost of evaluation. For a variety of reasons, reliable techniques to remove bloat are highly desirable – to simplify the solutions generated at the end of runs, so that there is some chance of understanding them, to permit systematic study of the evolution of the effective core of the genotype, or even to perform simplification of expressions during the course of a run. This paper introduces an alternative approach, Equivalent Decision Simplification, in which subtrees are evaluated over the set of regression points; if the subtrees evaluate to the same values as known simple subtrees, they are replaced. The effectiveness of the proposed method is confirmed by computer simulation taking simple Symbolic Regression problems as examples.

A comparison study between genetic algorithms and bayesian optimize algorithms by novel indices

Genetic Algorithms (GAs) are a search and optimization technique based on the mechanism of evolution. Recently, another sort of population-based optimization method called Estimation of Distribution Algorithms (EDAs) have been proposed to solve the GAs defects. Although several comparison studies between GAs and EDAs have been made, little is known about differences of statistical features between them. In this paper, we propose new statistical indices which are based on the concepts of crossover and mutation, used in GAs, to analyze the behavior of the population based optimization techniques. We also show simple results of comparison studies between GAs and the Bayesian Optimization Algorithm (BOA), a well-known Estimation of Distribution Algorithms (EDAs).

The Evolutionary Deep Learning based on Deep Convolutional Neural Network for the Anime Storyboard Recognition

Recently, the researches of image recognition have been developed remarkably by means of the deep learning. In this study, we focused on the anime storyboards and applied deep convolutional neural networks (DCNNs) to those data. There exists one problem that it takes a lot of effort to tune DCNN hyperparameters. To solve this problem, we propose a novel method called evolutionary the deep learning (evoDL) by means of genetic algorithms (GAs). The effectiveness of evoDL is confirmed by computer simulations taking a real anime storyboard recognition problem as an example.