Artificial Intelligent Systems and Machine Learning

Geeta Jangra ¹, Rashmi Gupta ¹

Affiliations
1 Department of Computer Engineering, University Institute of Engineering and Technology, Kurukshetra University, Kurukshetra, IN

Abstract

The Metaheuristic is a set of algorithmic concepts that can be used to define heuristic methods applicable to a wide set of different optimization problems such as routing, scheduling etc. This paper focuses on the comparative analysis of most successful methods of optimization techniques such as genetic algorithm (GA) and ant colony optimization (ACO) inspired by biological behavior. The main objective GA and ACO is to generate an optimal schedule so as to complete the tasks in minimum period of time as well as utilizing the resources in an efficient way.

Keywords

Meta-Heuristic Algorithms, Genetic Algorithms, Ant Colony Optimization.

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Chander Diwaker ¹, Upender Dhull ², Geeta Jangra ¹

Affiliations
1 Department of Computer Engineering, University Institute of Engineering and Technology, Kurukshetra University, Kurukshetra, IN
2 Department of Mechanical Engineering, University Institute of Engineering and Technology, Kurukshetra University, Kurukshetra, IN

Abstract

The Genetic Algorithms (GA) paradigm is being used increasingly in search and optimization problems. A genetic algorithm is an algorithm which varies a set of parameters and evaluates the quality or "fitness" of the results of a computation as the parameters are changed or "evolved". A study of the effect of Sigma scaling on the performance of Genetic Algorithm (GA) is reported here. This paper shows how the Sigma Scaling affects the performance of Genetic Algorithm by calculating the average and maximum fitness values. It is beneficial to use sigma scaling with genetic algorithms for three reasons. It prevents the Genetic Algorithm from premature convergence, maintains diversity in the population and always improves the performance of GA. Premature convergence is one of the major problems usually associated with the use of GA. This problem is tightly related with the loss of genetic diversity of the GA's population, being the cause of a decrease on the quality of the solutions found.

Keywords

Crossover, Fitness Function, Genetic Algorithm, Mutation, Sigma Scaling, Selection.

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