Parent Selection Operators for Genetic Algorithms (original) (raw)

IJERT-Parent Selection Operators for Genetic Algorithms

International Journal of Engineering Research and Technology (IJERT), 2013

https://www.ijert.org/parent-selection-operators-for-genetic-algorithms https://www.ijert.org/research/parent-selection-operators-for-genetic-algorithms-IJERTV2IS110523.pdf In this paper, an experimental study of six well known selection methods has conducted to a new technique of selection. Dynamic selection (DS), the proposed technique, exploits the advantages of each selection methods in terms of quality of solution and population diversity. Indeed, dynamic selection is based on two parameters that allow to decide the quality of candidate solutions and the genotypic diversity. The famous 0-1 Knapsack Problem is used to illustrate the efficiency of DS.

To improve the performance of genetic algorithms by using a novel selection operator

2023

The Genetic Algorithm (GA) was developed as a search engine for difficult non-deterministic polynomial optimization problems. However, it suffers from internal weaknesses, such as premature convergence and low computation efficiency. One critical aspect of the GA is the selection process, which determines new paths and ultimately guides the algorithm towards a solution. This paper details a novel selection procedure that is a perfect blend of the two extremes, namely exploitation and exploration. The proposed technique eliminates the fitness scaling problem by changing the selection pressure continuously during the selection stage. Utilizing traveling salesman problem library (TSPLIB) instances, a performance comparison of the proposed method with a few traditional selection methods was conducted, and the proposed strategy yielded much better outcomes in the form of standard deviations and mean values. A two-sided ttest was also developed, and the results revealed that the proposed strategy enhanced the performance of a GA substantially.

Analysis of Selection Schemes for Solving an Optimization Problem in Genetic Algorithm

International Journal of Computer Applications, 2014

Genetic algorithm is a population based an adaptive search and optimizations techniques and genetic mimic the natural evolution process. The Genetic operators include selection, crossover and mutation. The aim to present this paper is it gives comparative selection strategies for solving an optimization problem in genetic algorithm and evaluates their performance.

Selection Methods for Genetic Algorithms

2013

Based on a study of six well known selection methods often used in genetic algorithms, this paper presents a technique that benefits their advantages in terms of the quality of solutions and the genetic diversity. The numerical results show the extent to which the quality of solution depends on the choice of the selection method. The proposed technique, that can help reduce this dependence, is presented and its efficiency is numerically illustrated.

Offspring selection: A new self-adaptive selection scheme for genetic algorithms

2005

In terms of goal orientedness, selection is the driving force of Genetic Algorithms (GAs). In contrast to crossover and mutation, selection is completely generic, i.e. independent of the actually employed problem and its representation. GA-selection is usually implemented as selection for reproduction (parent selection). In this paper we propose a second selection step after reproduction which is also absolutely problem independent. This self-adaptive selection mechanism, which will be referred to as offspring selection, is closely related to the general selection model of population genetics. As the problem-and representation-specific implementation of reproduction in GAs (crossover) is often critical in terms of preservation of essential genetic information, offspring selection has proven to be very suited for improving the global solution quality and robustness concerning parameter settings and operators of GAs in various fields of applications. The experimental part of the paper discusses the potential of the new selection model exemplarily on the basis of standardized real-valued test functions in high dimensions.

A Comparative Study Using Genetic Algorithms to Deal with Dynamic Environments

Artificial Neural Nets and Genetic Algorithms, 2003

One of the approaches used in Evolutionary Algorithms (EAs) for problems in which the environment changes from time to time is to use techniques that preserve the diversity in population. We have tested and compared several algorithms that try to keep the population as diverse as possible. One of those approaches applies a new biologically inspired genetic operator called transformation, previously used with success in static optimization problems. We tested two EAs using transformation and two other classical approaches: random immigrants and hypermutation. The comparative study was made using the dynamic 0/1 Knapsack optimization problem. Depending on the characteristics of the dynamic changes, the best results were obtained with transformation or with hypermutation.

Learning Selection Strategies for Evolutionary Algorithms

Lecture Notes in Computer Science, 2014

Hyper-Heuristics is a recent area of research concerned with the automatic design of algorithms. In this paper we propose a grammarbased hyper-heuristic to automate the design of an Evolutionary Algorithm component, namely the parent selection mechanism. More precisely, we present a grammar that defines the number of individuals that should be selected, and how they should be chosen in order to adjust the selective pressure. Knapsack Problems are used to assess the capacity to evolve selection strategies. The results obtained show that the proposed approach is able to evolve general selection methods that are competitive with the ones usually described in the literature.

Performance Evaluation of Best-Worst Selection Criteria for Genetic Algorithm

Mathematics and Computer Science, 2017

Genetic algorithm's performance is based on three major factors, which are selection criteria, crossover and mutation operators. Each factor has its own significant role but the selection criteria to choose parents from the population is the key role to running the genetic algorithm. There is a number of selection schemes that have been introduced in literature and all have their own advantages. Most of the selection criterion is chose the parents which give highly optimum values based on the theory that healthy parents produce healthy offspring. In the current study, we proposed a new selection scheme which selects healthy parents as well as unhealthy parents. The novel selection scheme is simple to implement, and it has notable ability to reduce the effected of premature convergence compared to other selection schemes. We apply this new technique along with some traditional selection schemes on six benchmark problems and Simulation studies show a remarkable performance of the proposed selection scheme.

Study on Selection Methods of Parents and Crossover in Genetic Algorithm

International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 2021

Genetic Algorithms are the population based search and optimization technique that mimic the process of Genetic and Natural Evolution. Genetic algorithms are very effective way of finding an Optimized solution to a complex problem. Performance of genetic algorithms mainly depends on various factors such as selection of efficient parents and type of genetic operators which involve crossover and mutation operators etc. This paper will help the people to acquire the knowledge about various strategies of selecting parents and description about standard crossover operators.