Norberto Eiji Nawa

Fuzzy system parameters discovery by bacterial evolutionary algorithm

This paper presents a new method for discovering the parameters of a fuzzy system; namely, the combination of input variables of the rules, the parameters of the membership functions of the variables, and a set of relevant rules from numerical data using the newly proposed bacterial evolutionary algorithm (BEA). Nawa et al. (1997) proposed the pseudobacterial genetic algorithm (PBGA) that incorporates a modified mutation operator called bacterial mutation, based on a biological phenomenon of microbial evolution. The BEA has the same features of the PBGA, but introduces a new operation, called gene transfer operation, equally inspired by a microbial evolution phenomenon. While the bacterial mutation performs local optimization within the limits of a single chromosome, the gene transfer operation allows the chromosomes to directly transfer information to the other counterparts in the population. The gene transfer is inspired by the phenomenon of transfer of strands of genes in a population of bacteria. By means of this mechanism, one bacterium can rapidly spread its genetic information to other cells. Numerical experiments were performed to show the effectiveness of the BEA. The obtained results show the benefits that can be obtained with this method.

A study on fuzzy rules discovery using Pseudo-Bacterial Genetic Algorithm with adaptive operator

This paper presents a new operator called adaptive operator for the Pseudo-Bacterial Genetic Algorithm (PBGA). The PBGA was proposed by the authors as a new approach combining a genetic algorithm (GA) with a local improvement mechanism inspired by a process in bacterial genetics. The PBGA was applied for the discovery of fuzzy rules. The aim of the newly introduced adaptive operator is to improve the quality of the generated fuzzy rules, producing blocks of effective rules and more compact rule bases. The new operator adaptively decides the division points of each chromosome for the bacterial mutation and the cutting points for the crossover. In order to verify the efficiency of the proposed adaptive operator, the PBGA is applied to a simple fuzzy modeling problem. The new operator actuates according to the distribution of degrees of truth values of the rules. The results show the benefits that can be obtained with this operator.

A study on the discovery of relevant fuzzy rules using pseudobacterial genetic algorithm

This paper presents a new method for the discovery of relevant fuzzy rules using the pseudobacterial genetic algorithm (PBGA). The PBGA was proposed by the authors as a new approach combining a genetic algorithm with a local improvement mechanism inspired by a process in bacterial genetics, named bacterial operation. The presented system aims at the improvement of the quality of the generated fuzzy rules, producing blocks of effective rules and more compact rule bases. This is achieved by encoding the fuzzy rules in the chromosomes in a suitable form in order to make the bacterial operation more effective and by using a crossover operation that adaptively decides the cutting points according to the distribution of degrees of truth values of the rules. In this paper, first, results obtained when using the PBGA for a simple fuzzy modeling problem are presented and compared with other methods. Second, the PBGA is used in the design of a fuzzy logic controller for a semi-active suspension system. The results show the benefits obtained with this approach in both of the studied cases.

A ``Spike Interval Information Coding'' Representation for ATR's CAM-Brain Machine (CBM)

This paper reports on ongoing attempts to find an efficient and effective representation for the binary signaling of ATR’s CAM-Brain Machine (CBM), using the so-called ”CoDi-1Bit” model. The CBM is an Field Programmable Gate Array (FPGA) based hardware accelerator which updates 3D cellular automata (CA) cells at the rate of 100 billion a second, allowing a complete run of a genetic algorithm with tens of thousands of CA based neural net circuit growths and hardware compiled fitness evaluations, all in about 1 second. It is hoped that using such a device, it will become practical to evolve 10,000s of neural net modules and then to assemble them into humanly defined RAM based artificial brain architectures which can be run by the CBM in real time to control robots, e.g. a robot kitten. Before large numbers of modules can be assembled together, it is essential that the individual modules have a good functionality and evolvability. The ”CoDi-1Bit” CA based neural network model uses 1 bit binary signaling, so a representation needs to be chosen based on this fact. This paper introduces and discusses the merits and demerits of a representation that we call ”Spike Interval Information Coding” (SIIC). Simulation results using the SIIC representation method to evolve time dependent waveforms and simple functional modules are presented. The results indicate the suitability of the SIIC representation method to decode the bit streams generated by the CA based neural networks.

A study on the effect of transfer of genes for the bacterial evolutionary algorithm

This paper presents a study on the effect of transfer of genes for bacterial evolutionary algorithm. The bacterial operation, in the pseudo-bacterial genetic algorithm (PBGA) is efficient in improving local portions of chromosomes. In the experiments presented a gene transfer operation is introduced in the place of the crossover operation in the PBGA. The gene transfer operation allows the chromosomes to directly transfer information to the other counterparts in the population. The gene transfer is inspired by the phenomenon of transfer of strands of genes between bacteria that occurs in nature. By means of this mechanism. One bacterium can rapidly spread its genetic information to other cells. Numerical experiments were performed to show the effectiveness of the PBGA with the gene transfer operation. The obtained results show the benefits that can be obtained with the newly introduced operation.

Bacterial evolutionary algorithm for fuzzy system design

Presents a method for discovering the parameters of a fuzzy system, namely the combination of input variables of the rules, the parameters of the membership functions of the variables and a set of relevant rules, from numerical data using the newly proposed bacterial evolutionary algorithm (BEA). In early work, the authors proposed the pseudo-bacterial genetic algorithm (PBGA) that incorporates a modified mutation operator called bacterial mutation, based on a natural phenomenon of microbial evolution. The BEA has the same features of the PBGA, but introduces a new operator, called gene transfer operation, equally inspired by a microbial evolution phenomenon. While the bacterial mutation performs local optimization within the limits of a single chromosome, the gene transfer operation allows the chromosomes to directly transfer information to the other counterparts in the population. The gene transfer is inspired by the natural phenomenon of transfer of strands of genes between bacteria in a population. By means of this mechanism, one bacterium can rapidly spread its genetic information to other cells. Numerical experiments were performed to show the effectiveness of the BEA. The obtained results show the benefits that can be obtained with the newly proposed method.

Fuzzy logic controllers generated by pseudo-bacterial genetic algorithm with adaptive operator

This paper presents a new genetic operator called adaptive operator to improve local portions of chromesomes. This new operator is implemented in a pseudo-bacterial genetic algorithm (PBGA). The PBGA was proposed by the authors as a new approach combining a genetic algorithm (GA) with a local improvement mechanism inspired by a process in bacterial genetics. The PBGA was applied for the acquisition of fuzzy rules. The aim of the newly introduced adaptive operator is to improve the quality of the generated rules of the fuzzy models, producing blocks of effective rules and more compact models. The new operator adaptively decides the division points of each chromosome for the bacterial mutation and the cutting points for the crossover, according to the distribution of degrees of truth values of the rules. In this paper, first, results obtained when using the PBGA with the adaptive operator for a simple fuzzy modeling problem are presented. Second, the PBGA with adaptive operator is used in the design of a fuzzy logic controller for a semi-active suspension system. The results show the benefits obtained with this operator.

ATR's Artificial Brain (CAM-Brain) Project: a progress report

The paper reports on recent progress made in ATRs attempt to build a 10,000 evolved neural net module artificial brain to control the behavior of a life sized robot kitten.