Hill Climbing Based Hybrid Crossover in Genetic Algorithms
نویسندگان
چکیده
Genetic Algorithms are biologically inspired optimization algorithms. Performance of genetic algorithms mainly depends on type of genetic operators – Selection, Crossover, Mutation and Replacement used in it. Crossover operators are used to bring diversity in the population. This paper studies different crossover operators and then proposes a hybrid crossover operator that incorporates knowledge based on existing population and uses the concept of Hill climbing search. Performance of the proposed hill climbing based hybrid crossover is compared with existing PMX and OX operator in genetic algorithm. Implementation is carried out in MATLAB on benchmark TSP Oliver30 problem. The results are optimistic and clearly demonstrate that the proposed hybrid crossover is better than the existing crossovers in terms of convergence towards optimal solution. Keywords— Crossover, genetic algorithm, hill climbing, memetic algorithm.
منابع مشابه
Comparison of Genetic and Hill Climbing Algorithms to Improve an Artificial Neural Networks Model for Water Consumption Prediction
No unique method has been so far specified for determining the number of neurons in hidden layers of Multi-Layer Perceptron (MLP) neural networks used for prediction. The present research is intended to optimize the number of neurons using two meta-heuristic procedures namely genetic and hill climbing algorithms. The data used in the present research for prediction are consumption data of water...
متن کاملHybrid Genetic Algorithm and Mixed Crossover Operator for Optimizing TSP
Genetic Algorithms (GAs) are the search algorithms and optimization techniques based on the mechanics of natural selection and natural genetics. They sort out interesting areas of a space quickly but without guaranteeing more convergence. So GA may be mixed with various local problem-specific search techniques to form a hybrid that will combine the globality and parallelism of GA with more conv...
متن کاملMemetic Algorithms for Dynamic Optimization Problems
Dynamic optimization problems challenge traditional evolutionary algorithms seriously since they, once converged, cannot adapt quickly to environmental changes. This chapter investigates the application of memetic algorithms, a class of hybrid evolutionary algorithms, for dynamic optimization problems. An adaptive hill climbing method is proposed as the local search technique in the framework o...
متن کاملGenetic Algorithms: Combining Evolutionary and `Non'-Evolutionary Methods in Tracking Dynamic Global Optima
The ability to track dynamic functional optima is important in many practical tasks. Recent research in this area has concentrated on modifying evolutionary algorithms (EAs) by triggering changes in control parameters, ensuring population diversity, or remembering past solutions. A set of results are presented that favourably compare hill climbing with a genetic algorithm, and reasons for the r...
متن کاملHybridized Crossover-Based Search Techniques for Program Discovery
In this paper we address the problem of program discovery as deened by Genetic Programming 10]. We have two major results: First, by combining a hierarchical crossover operator with two traditional single point search algorithms: Simulated Annealing and Stochastic Iterated Hill Climbing, we have solved some problems with fewer tness evaluations and a greater probability of a success than Geneti...
متن کامل