Theory of fuzzy integrals and its applications

Evolutionary computation has started to receive significant attention during the last decade, although the origins can be traced back to the late 1950's. This article surveys the history as well as the current state of this rapidly growing field. We describe the purpose, the general structure, and the working principles of different approaches, including genetic algorithms (GA) (with links to genetic programming (GP) and classifier systems (CS)), evolution strategies (ES), and evolutionary programming (EP) by analysis and comparison of their most important constituents (i.e. representations, variation operators, reproduction, and selection mechanism). Finally, we give a brief overview on the manifold of application domains, although this necessarily must remain incomplete.

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Evolutionary computation: comments on the history and current state

In this paper, the analysis of recent advances in genetic algorithms is discussed. The genetic algorithms of great interest in research community are selected for analysis. This review will help the new and demanding researchers to provide the wider vision of genetic algorithms. The well-known algorithms and their implementation are presented with their pros and cons. The genetic operators and their usages are discussed with the aim of facilitating new researchers. The different research domains involved in genetic algorithms are covered. The future research directions in the area of genetic operators, fitness function and hybrid algorithms are discussed. This structured review will be helpful for research and graduate teaching.

/pdf/a-review-on-genetic-algorithm-past-present-and-future-2o5y8ru178.pdf

A review on genetic algorithm: past, present, and future

Genetic algorithms (GAs) are powerful search techniques that are used successfully to solve problems in many different disciplines. Parallel GAs are particularly easy to implement and promise substantial gains in performance. As such, there has been extensive research in this field. This survey attempts to collect, organize, and present in a unified way some of the most representative publications on parallel genetic algorithms. To organize the literature, the paper presents a categorization of the techniques used to parallelize GAs, and shows examples of all of them. However, since the majority of the research in this field has concentrated on parallel GAs with multiple populations, the survey focuses on this type of algorithms. Also, the paper describes some of the most significant problems in modeling and designing multi-population parallel GAs and presents some recent advancements.

/pdf/a-survey-of-parallel-genetic-algorithms-rh37ea0cwr.pdf

A Survey of Parallel Genetic Algorithms

Major histocompatibility complex (MHC) genes (HLA in humans) regulate the immune response to foreign antigens Molecular and serologic techniques were used to identify products of HLA class I, class II and transporter (TAP) genes (also part of the MHC) in homosexual seroconverters to human immunodeficiency virus type 1 (HIV-1) Comprehensive statistical analysis produced an HLA profile that predicted time from HIV-1 infection to the onset of AIDS The profile was developed in a cohort of 139 men and evaluated in a second unrelated cohort of 102 men In the evaluation cohort, the profile discriminated a sixfold difference between groups with the shortest and longest times to AIDS (P = 0001) These findings support current theory about control of antigen processing by HLA genes and have implications for immunopathogenesis of HIV-1 and other infections

Influence of combinations of human major histocompatibility complex genes on the course of HIV–1 infection

This paper presents a string migration scheme for an adaptive network routing algorithm called a genetic routing algorithm which employs genetic operators to create alternative routes in a routing table. String migrations are employed usually in islands model of parallel or distributed genetic algorithms, which exchange strings among subpopulations to accelerate their convergence. We propose a tailored version of string migration for the genetic routing algorithm in order to realize effective information exchanges among nodes to have optimal route with less communication overhead in the network.

A migration scheme for the genetic adaptive routing algorithm

An Efficient Migration Scheme for Subpopulation-Based Asynchronously Parallel Genetic Algorithms

An Adaptive Network Routing Algorithm Employing Path Genetic Operators.

The geometrical structure of the parameter space of the two-dimensional normal distribution

In this paper, we discuss the fuzzy clustering for 3-way data which is composed of objects, attributes and situations. In the case of 2-way data, the clustering of objects are usually based on the values of attributes. However, for 3-way data, a clustering of objects is not always coincident for all situations, because the values of attributes vary with each situation. Therefore, the clustering problem for 3-way data can be regarded as a multicriteria optimization problem. It is known that the practical solutions for a multicriteria optimization problem are Pareto efficient solutions. In a multicriteria hard (non-fuzzy) clustering problem, it is difficult to find a Pareto efficient cluster since this is essentially combinatorial problem. But we show that a multicriteria fuzzy clustering, which is an extension of this hard clustering problem, has merit to obtain Pareto efficient clusters. We investigate the features for Pareto efficient clusters using an artificial 3-way data and we show a numerical example applied to the data of the growth of physical constitutions.

Yoshiharu Sato

Papers

A migration scheme for the genetic adaptive routing algorithm

An Efficient Migration Scheme for Subpopulation-Based Asynchronously Parallel Genetic Algorithms

An Adaptive Network Routing Algorithm Employing Path Genetic Operators.

The geometrical structure of the parameter space of the two-dimensional normal distribution

On a multicriteria fuzzy clustering method for 3-way data