Effective EV Population Initialization Technique for Genetic Algorithm
نویسندگان
چکیده
In traditional Genetic Algorithm, random population seeding technique is simple and efficient however the population may contain poor quality individuals which take long time to converge optimal solution. This motivates to design a population initialization technique with the features of randomness, individual diversity and good quality. In this paper, an initial work has been carried out to develop an innovative Equi-begin and Vari-diversity (EV) population seeding technique. Experimentation is performed on Travelling Salesman Problem instances, based on convergence rate, obtained from TSPLIB using MATLAB shows the developed population initialization technique can produce the individuals with high fitness. Keywords—population seeding, genetic algorithm, convergence rate,TSP
منابع مشابه
مدل حل مبتنی بر جستجوگر محلی ژنتیک برای مساله زمان بندی استقرار کارگاهی تعمیم یافته با زمانهای عملیات قابل کنترل
Although incorporating complexities and flexibilities of real world manufacturing systems into classic scheduling problems results in problems with greater complexity, it has immense theoretical and practical importance due to its impressive effect on system performance. In this research, three basic assumptions of a job shop scheduling problem have been revised to develop a model with three ty...
متن کاملUsing Population Based Algorithms for Initializing Nonnegative Matrix Factorization
The nonnegative matrix factorization (NMF) is a boundconstrained low-rank approximation technique for nonnegative multivariate data. NMF has been studied extensively over the last years, but an important aspect which only has received little attention so far is a proper initialization of the NMF factors in order to achieve a faster error reduction. Since the NMF objective function is usually no...
متن کاملAn Enhanced K Means Clustering using Improved Hopfield Artificial Neural Network and Genetic Algorithm
Due to the increase in the quantity of data across the world, it turns out to be very complex task for analyzing those data. Categorize those data into remarkable collection is one of the common forms of understanding and learning. This leads to the requirement for better data mining technique. These facilities are provided by a standard data mining technique called Clustering. The key intentio...
متن کاملEMCSO: An Elitist Multi-Objective Cat Swarm Optimization
This paper introduces a novel multi-objective evolutionary algorithm based on cat swarm optimizationalgorithm (EMCSO) and its application to solve a multi-objective knapsack problem. The multi-objective optimizers try to find the closest solutions to true Pareto front (POF) where it will be achieved by finding the less-crowded non-dominated solutions. The proposed method applies cat swarm optim...
متن کاملSolving Large Travelling Salesman Problems with Small Populations
A new genetic algorithm for the solution of the travelling salesman problem is presented in this paper. The approach is to introduce several knowledge-augmented genetic operators which guide the genetic algorithm more directly towards better quality of the populationbut are not trapped in local optima prematurely. The algorithm applies a greedy crossover and two advanced mutation operations bas...
متن کامل