A Simple Genetic Algorithm Implementation (original) (raw)

A Review Paper on Brief Introduction of Genetic Algorithm

In this paper we discuss about basics of genetic algorithm. Genetic algorithm is search and optimization technique that produce optimization of problem by using natural evolution. Genetic algorithm mainly depends on best chosen chromosomes from various steps that described by author in this paper. Optimization is an act, process, or methodology of making something such as design, system, or design.

Genetic Algorithms: Basic Concepts and Real World Applications

This paper introduces Genetic algorithms which is a part of evolutionary computing techniques. It is specially invented for development of natural selection and genetic evaluation. Genetic algorithms are an emerging technology for basic algorithms used to generate solution and one of the most efficient tools for solving optimization problem. The purpose of this paper is to provide solution for the real life problems which are always an immense challenge for researchers. The genetic algorithms are search and optimization algorithms based on the principles of natural selection and genetic evolution.

A Study on Genetic Algorithm and its Applications

— In order to obtain best solutions, we need a measure for differentiating best solutions from worst solutions. The measure could be an objective one that is a statistical model or a simulation, or it can be a subjective one where we choose better solutions over worst ones. Apart from this the fitness function determines a best solution for a given problem, which is subsequently used by the GA to guide the evolution of best solutions. This paper shows how GA is combined with various other methods and technique to derive optimal solution, increase the computation time of retrieval system the applications of genetic algorithms in various fields.

TECHNOLOGY A Review on Genetic Algorithm

2014

This paper covers the canonical genetic algorithm as well as more experimental forms of genetic algorithms, including parallel island models and parallel cellular genetic algorithms. The theoretical foundations of genetic algorithms are reviewed, include the schema theorem as well as recently developed exact models of the canonical genetic algorithms.

Application of Genetic Algorithm in Common Optimization Problems

International Annals of Science, 2019

Use of non-deterministic algorithms for solving multi-variable optimization problems is widely used nowadays. Genetic Algorithm belongs to a group of stochastic biomimicry algorithms, it allows us to achieve optimal or near-optimal results in large optimization problems in exceptionally short time (compared to standard optimization methods). Major advantage of Genetic Algorithm is the ability to fuse genes, to mutate and do selection based on fitness parameter. These methods protect us from being trapped in local optima (Most of deterministic algorithms are prone to getting stuck on local optima). In this paper we experimentally show the upper hand of Genetic Algorithms compared to other traditional optimization methods by solving complex optimization problem.

A Novel Genetic Algorithm based approach for Optimized Solution

isara solutions, 2016

Genetic Algorithm (GA) is a search-based optimization technique based on the principles of Genetics and Natural Selection. It is frequently used to find optimal or near-optimal solutions to difficult problems which otherwise would take a lifetime to solve. It is frequently used to solve optimization problems, in research, and in machine learning.Nature has always been a great source of inspiration to all mankind. Genetic Algorithms (GAs) are search based algorithms based on the concepts of natural selection and genetics.In GAs, we have a pool or a population of possible solutions to the given problem. These solutions then undergo recombination and mutation (like in natural genetics), producing new children, and the process is repeated over various generations. Each individual (or candidate solution) is assigned a fitness value (based on its objective function value) and thefitter individuals are given a higher chance to mate and yield more “fitter” individuals.

A Review on Genetic Algorithm.

International Journal of Engineering Sciences & Research Technology, 2014

This paper covers the canonical genetic algorithm as well as more experimental forms of genetic algorithms, including parallel island models and parallel cellular genetic algorithms. The theoretical foundations of genetic algorithms are reviewed, include the schema theorem as well as recently developed exact models of the canonical genetic algorithms.

GENETIC ALGORITHM FOR OPTIMIZATION PROBLEMS

Decision making features occur in all fields of human activities such as science and technological and affect every sphere of our life. Normally, any engineering problem will have a large number of solutions out of which some are feasible an d some are non - feasible. The designer’s task is to get best solution out of the feasible solutions. The complete set of feasible solutions constitutes feasible design space and progress towards the optimal design. In such a case, genetic algorithms are goo d at taking larger, potentially huge search space and navigating them looking for optimal combinations of things and solutions that may not be find in a life time. Genetic algorithm unlike traditional optimization methods processes a number of designs at s ame time, uses randomized operators that improves search space with efficient result. This paper dealt with important aspects of GA that includes definition of objective function, representation schemas for solution variables and randomized operators. Thes e aspects drive the problem to optimal solution.