New Genetic Algorithm (new + genetic_algorithm)

Distribution by Scientific Domains

Selected Abstracts

A new genetic algorithm with diploid chromosomes using probability decoding for adaptation to various environments

Manabu Kominami
Abstract This paper proposes a new diploid operation technique using probability for function optimization in nonstationary environments and describes a feature of diploid genetic algorithms (GAs). The advantage of the technique over previous diploid GAs is that one genotype is transformed into many phenotypes based on probability. This transformation is not made at random. It has a certain range of probabilities. Each individual has a range. The range allows adaptation to various environments. The technique allows genes to give a probabilistic representation of dominance, and can maintain the diversity of individuals. The experimental results show that the technique can adapt to severe environmental changes where previous diploid GAs cannot adapt. This paper shows that the technique can find optimum solutions with high probability and that the distribution of individuals changes when the environment changes. In addition, by comparing the proposed diploid GA with a haploid GA whose chromosome is twice the length, the features of the diploid are described. © 2010 Wiley Periodicals, Inc. Electron Comm Jpn, 93(8): 38,46, 2010; Published online in Wiley InterScience ( DOI 10.1002/ecj.10097 [source]

Minimisation of end-to-end delay in reconfigurable WDM networks using genetic algorithms

Ramón J. Durán Barroso
A new genetic algorithm (GA) is proposed to design logical topologies for wavelength-routed optical networks (WRONs) with the objective of minimising the end-to-end delay. Two versions of the algorithm, called D-GALD (Delay-optimised Genetic Algorithm for Logical topology Design), have been developed. The first one minimises the average end-to-end delay of the packets transported by the network, while the second one minimises the average delay of the most delayed traffic flow. By means of a simulation study, we show that the logical topologies designed by D-GALD support more than 50 per cent higher traffic load,without causing network instability,than those ones designed by other heuristics. Moreover, the utilisation of D-GALD leads to reductions of up to 15 per cent in the average end-to-end delay and around 30 per cent in the average end-to-end delay of the most delayed traffic flow. Copyright © 2008 John Wiley & Sons, Ltd. [source]

Multiscale multiresolution genetic algorithm with a golden sectioned population composition

Dae Seung Kim
Abstract A new genetic algorithm (GA) strategy called the multiscale multiresolution GA is proposed for expediting solution convergence by orders of magnitude. The motivation for this development was to apply GAs to a certain class of large optimization problems, which are otherwise nearly impossible to solve. For the algorithm, standard binary design variables are binary wavelet transformed to multiscale design variables. By working with the multiscale variables, evolution can proceed in multiresolution; converged solutions at a low resolution are reused as a part of individuals of the initial population for the next resolution evolution. It is shown that the best solution convergence can be achieved if three initial population groups having different fitness levels are mixed at the golden section ratio. An analogy between cell division and the proposed multiscale multiresolution strategy is made. The specific applications of the developed method are made in topology optimization problems. Copyright © 2007 John Wiley & Sons, Ltd. [source]

An evolutionary algorithm for constructing a decision forest: Combining the classification of disjoints decision trees

Lior Rokach
Decision forest is an ensemble classification method that combines multiple decision trees to in a manner that results in more accurate classifications. By combining multiple heterogeneous decision trees, decision forest is effective in mitigating noise that is often prevalent in real-world classification tasks. This paper presents a new genetic algorithm for constructing a decision forest. Each decision tree classifier is trained using a disjoint set of attributes. Moreover, we examine the effectiveness of using a Vapnik,Chervonenkis dimension bound for evaluating the fitness function of decision forest. The new algorithm was tested on various datasets. The obtained results have been compared to other methods, indicating the superiority of the proposed algorithm. © 2008 Wiley Periodicals, Inc. [source]

Evolutionary combinatorial chemistry, a novel tool for SAR studies on peptide transport across the blood,brain barrier.

Part 2.
Abstract The use of high-throughput methods in drug discovery allows the generation and testing of a large number of compounds, but at the price of providing redundant information. Evolutionary combinatorial chemistry combines the selection and synthesis of biologically active compounds with artificial intelligence optimization methods, such as genetic algorithms (GA). Drug candidates for the treatment of central nervous system (CNS) disorders must overcome the blood,brain barrier (BBB). This paper reports a new genetic algorithm that searches for the optimal physicochemical properties for peptide transport across the blood,brain barrier. A first generation of peptides has been generated and synthesized. Due to the high content of N -methyl amino acids present in most of these peptides, their syntheses were especially challenging due to over-incorporations, deletions and DKP formations. Distinct fragmentation patterns during peptide cleavage have been identified. The first generation of peptides has been studied by evaluation techniques such as immobilized artificial membrane chromatography (IAMC), a cell-based assay, log Poctanol/water calculations, etc. Finally, a second generation has been proposed. Copyright © 2005 European Peptide Society and John Wiley & Sons, Ltd. [source]