Specific Evolutionary Algorithms

1996 ◽  
Author(s):  
Thomas Bäck
Keyword(s):  
Genetic Algorithms ◽  
Evolutionary Algorithms ◽  
Selection Process ◽  
Evolutionary Programming ◽  
Main Stream ◽  
Genetic Operators ◽  
Optimization Task ◽  
Main Components ◽  
High Level

In this chapter, an outline of an Evolutionary Algorithm is formulated that is sufficiently general to cover at least the three different main stream algorithms mentioned before, namely, Evolution Strategies, Genetic Algorithms, and Evolutionary Programming. As in the previous chapter, algorithms are formulated in a language obtained by mixing pseudocode and mathematical notations, thus allowing for a high-level description which concentrates on the main components. These are: A population of individuals which is manipulated by genetic operators — especially mutation and recombination, but others may also be incorporated — and undergoes a fitness-based selection process, where fitness of an individual depends on its quality with respect to the optimization task.

scholarly journals EVOLUTIONARY ALORITHMS AT CHOICE: FROM GA TO GP

2015 ◽  
Vol 2 ◽  
pp. 185 ◽  
Author(s):  
Peter Grabusts
Keyword(s):  
Genetic Algorithms ◽  
Evolutionary Algorithms ◽  
Genetic Programming ◽  
Evolutionary Programming ◽  
Stochastic Search ◽  
Evolution Strategies ◽  
Natural Evolution ◽  
Search Methods ◽  
The World ◽  
Classification Tasks

Nowadays the possibilities of evolutionary algorithms are widely used in many optimization and classification tasks. Evolutionary algorithms are stochastic search methods that try to emulate Darwin’s principle of natural evolution. There are (at least) four paradigms in the world of evolutionary algorithms: evolutionary programming, evolution strategies, genetic algorithms and genetic programming. This paper analyzes present-day approaches of genetic algorithms and genetic programming and examines the possibilities of genetic programming that will be used in further research. The paper presents implementation examples that show the working principles of evolutionary algorithms.

scholarly journals A Mathematical Design of Genetic Operators onGLn(ℤ2)

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Yourim Yoon ◽  
Yong-Hyuk Kim
Keyword(s):  
Genetic Algorithms ◽  
Evolutionary Algorithms ◽  
Linear Algebra ◽  
Binary Representation ◽  
Two Dimensional ◽  
Genetic Operators ◽  
Binary Matrices ◽  
Mathematical Design

We study the space that consists of all nonsingular binary matrices, that is,GLn(ℤ2). The space is quite important in that it is used for the change of basis in binary representation, which is the encoding typically adopted in genetic algorithms. We analyze the properties ofGLn(ℤ2)and theoretically design possible encodings and their corresponding recombination operators for evolutionary algorithms. We present approaches based on elementary matrices of linear algebra as well as typical two-dimensional ones.

scholarly journals Modeling of Biological Intelligence for SCM System Optimization

2012 ◽  
Vol 2012 ◽  
pp. 1-10 ◽  
Author(s):  
Shengyong Chen ◽  
Yujun Zheng ◽  
Carlo Cattani ◽  
Wanliang Wang
Keyword(s):  
Genetic Algorithms ◽  
Supply Chain ◽  
Evolutionary Algorithms ◽  
Differential Evolution ◽  
System Optimization ◽  
Evolutionary Programming ◽  
Artificial Immune ◽  
Chain Management ◽  
Design And Optimization ◽  
Information Materials

This article summarizes some methods from biological intelligence for modeling and optimization of supply chain management (SCM) systems, including genetic algorithms, evolutionary programming, differential evolution, swarm intelligence, artificial immune, and other biological intelligence related methods. An SCM system is adaptive, dynamic, open self-organizing, which is maintained by flows of information, materials, goods, funds, and energy. Traditional methods for modeling and optimizing complex SCM systems require huge amounts of computing resources, and biological intelligence-based solutions can often provide valuable alternatives for efficiently solving problems. The paper summarizes the recent related methods for the design and optimization of SCM systems, which covers the most widely used genetic algorithms and other evolutionary algorithms.

scholarly journals GenHap: A novel computational method based on genetic algorithms for haplotype assembly

2017 ◽  
Author(s):  
Andrea Tangherloni ◽  
Simone Spolaor ◽  
Leonardo Rundo ◽  
Marco S Nobile ◽  
Ivan Merelli ◽  
...  
Keyword(s):  
Genetic Algorithms ◽  
Selection Process ◽  
Fitness Function ◽  
Computational Method ◽  
Nucleotide Polymorphisms ◽  
Optimization Strategy ◽  
Genetic Operators ◽  
Chromosome Conformation ◽  
Haplotype Assembly ◽  
Fitness Value

The process of inferring a full haplotype of a cell is known as haplotyping, which consists in assigning all heterozygous Single Nucleotide Polymorphisms (SNPs) to exactly one of the two chromosomes. In this work, we propose a novel computational method for haplotype assembly based on Genetic Algorithms (GAs), named GenHap. Our approach could efficiently solve large instances of the weighted Minimum Error Correction (wMEC) problem, yielding optimal solutions by means of a global search process. wMEC consists in computing the two haplotypes that partition the sequencing reads into two unambiguous sets with the least number of corrections to the SNP values. Since wMEC was proven to be an NP-hard problem, we tackle this problem exploiting GAs, a population-based optimization strategy that mimics Darwinian processes. In GAs, a population composed of randomly generated individuals undergoes a selection mechanism and is modified by genetic operators. Based on a quality measure (i.e., the fitness value), inspired by Darwin’s “survival of the fittest” laws, each individual is involved in a selection process. Our preliminary experimental results show that GenHap is able to achieve correct solutions in short running times. Moreover, this approach can be used to compute haplotypes in organisms with different ploidity. The proposed evolutionary technique has the advantage that it could be formulated and extended using a multi-objective fitness function taking into account additional insights, such as the methylation patterns of the different chromosomes or the gene proximity in maps achieved through Chromosome Conformation Capture (3C) experiments.

scholarly journals GenHap: A novel computational method based on genetic algorithms for haplotype assembly

2017 ◽  
Author(s):  
Andrea Tangherloni ◽  
Simone Spolaor ◽  
Leonardo Rundo ◽  
Marco S Nobile ◽  
Ivan Merelli ◽  
...  
Keyword(s):  
Genetic Algorithms ◽  
Selection Process ◽  
Fitness Function ◽  
Computational Method ◽  
Nucleotide Polymorphisms ◽  
Optimization Strategy ◽  
Genetic Operators ◽  
Chromosome Conformation ◽  
Haplotype Assembly ◽  
Fitness Value

The process of inferring a full haplotype of a cell is known as haplotyping, which consists in assigning all heterozygous Single Nucleotide Polymorphisms (SNPs) to exactly one of the two chromosomes. In this work, we propose a novel computational method for haplotype assembly based on Genetic Algorithms (GAs), named GenHap. Our approach could efficiently solve large instances of the weighted Minimum Error Correction (wMEC) problem, yielding optimal solutions by means of a global search process. wMEC consists in computing the two haplotypes that partition the sequencing reads into two unambiguous sets with the least number of corrections to the SNP values. Since wMEC was proven to be an NP-hard problem, we tackle this problem exploiting GAs, a population-based optimization strategy that mimics Darwinian processes. In GAs, a population composed of randomly generated individuals undergoes a selection mechanism and is modified by genetic operators. Based on a quality measure (i.e., the fitness value), inspired by Darwin’s “survival of the fittest” laws, each individual is involved in a selection process. Our preliminary experimental results show that GenHap is able to achieve correct solutions in short running times. Moreover, this approach can be used to compute haplotypes in organisms with different ploidity. The proposed evolutionary technique has the advantage that it could be formulated and extended using a multi-objective fitness function taking into account additional insights, such as the methylation patterns of the different chromosomes or the gene proximity in maps achieved through Chromosome Conformation Capture (3C) experiments.

Minimization of total trim loss occurring in pipe cutting in shipbuilding with genetic algorithm

2021 ◽  
pp. 147509022199169
Author(s):  
Abdullah Türk ◽  
Dursun Saral ◽  
Murat Özkök ◽  
Ercan Köse
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Search Space ◽  
Cutting Process ◽  
Genetic Operators ◽  
Critical Stage ◽  
Different Types ◽  
Trim Loss ◽  
Pipe Systems

Outfitting is a critical stage in the shipbuilding process. Within the outfitting, the construction of pipe systems is a phase that has a significant effect on time and cost. While cutting the pipes required for the pipe systems in shipyards, the cutting process is usually performed randomly. This can result in large amounts of trim losses. In this paper, we present an approach to minimize these losses. With the proposed method it is aimed to base the pipe cutting process on a specific systematic. To solve this problem, Genetic Algorithms (GA), which gives successful results in solving many problems in the literature, have been used. Different types of genetic operators have been used to investigate the search space of the problem well. The results obtained have proven the effectiveness of the proposed approach.

scholarly journals Study of the industrial contaminations containing hydrocarbon components and their effect on correlation of protective properties of textiles for clothes

2018 ◽  
Vol 226 ◽  
pp. 02027
Author(s):  
Irina Cherunova ◽  
Elena Yakovleva ◽  
Ekaterina Stefanova
Keyword(s):  
Ministry Of Education ◽  
State Technical ◽  
Potential Influence ◽  
Saturated Hydrocarbons ◽  
Protective Properties ◽  
Textile Materials ◽  
Wet Processing ◽  
Main Components ◽  
High Level ◽  
Made In

The article represents trends and results of world investigations for safety of textile materials for humans and environment. The algorithm for identification of potential influence vector of chemical emissions from textile and the environmentally aimed methods based on the alternative chemical substances for textile wet processing were presented. Using the procedure for oleophobic effect determination, the oil resistance levels of a number of modern fabrics for clothes were established. The experimental research results of the oil products concentration in the textile materials after chemical treatment based on the method of the capillary gas chromatography were presented. It was found out that hydrocarbons from C14H30 Tetradecane to С36Н74 Hexatriacontane are the main components with high level of contaminants concentration. The average values of hydrocarbon concentration on the cleaned up samples for the main areas of the clothe surface were determined. Accumulation of residual contaminations, based on saturated hydrocarbons (С17Н36 – С25Н52 block) and related special-purpose clothes areas, was identified. Recommendations for target-oriented correlation of the clothes protective properties were proposed. UWO (unified work order). The research was made in Don State Technical University within the framework of State Assignment of the Ministry of education and science of Russia under the project 11.9194.2017/BCh.

Evolutionary Algorithms In Architecture

2011 ◽  
Vol 36 (1) ◽  
pp. 16-24
Author(s):  
Peter Schwehr
Keyword(s):  
Evolutionary Algorithms ◽  
Evolutionary Algorithm ◽  
Everyday Life ◽  
Working Model ◽  
Constant Change ◽  
Buckminster Fuller ◽  
High Level

Change is a reliable constant. Constant change calls for strategies in managing everyday life and a high level of flexibility. Architecture must also rise to this challenge. The architect Richard Buckminster Fuller claimed that “A room should not be fixed, should not create a static mood, but should lend itself to change so that its occupants may play upon it as they would upon a piano (Krausse 2001).” This liberal interpretation in architecture defines the ability of a building to react to (ever-) changing requirements. The aim of the project is to investigate the flexibility of buildings using evolutionary algorithms characterized by Darwin. As a working model for development, the evolutionary algorithm consists of variation, selection and reproduction (VSR algorithm). The result of a VSR algorithm is adaptability (Buskes 2008). If this working model is applied to architecture, it is possible to examine as to what extent the adaptability of buildings – as an expression of a cultural achievement – is subject to evolutionary principles, and in which area the model seems unsuitable for the 'open buildings' criteria. (N. John Habraken). It illustrates the significance of variation, selection and replication in architecture and how evolutionary principles can be transferred to the issues of flexible buildings. What are the consequences for the building if it were to be designed and built with the help of evolutionary principles? How can we react to the growing demand for flexibilization of buildings by using evolutionary principles?

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