Hybrid population-based incremental learning using real codes

Authors:
Sujin Bureerat
Affiliations:
Department of Mechanical Engineering, Faculty of Engineering, Khon Kaen University, Thailand
Venue:
LION'05 Proceedings of the 5th international conference on Learning and Intelligent Optimization
Year:
2011

Citing 7
Cited 2

Numerical methods using MATLAB

Numerical methods using MATLAB
Extending Population-Based Incremental Learning to Continuous Search Spaces

PPSN V Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
Population-Based Incremental Learning: A Method for Integrating Genetic Search Based Function Optimization and Competitive Learning

Population-Based Incremental Learning: A Method for Integrating Genetic Search Based Function Optimization and Competitive Learning
Linked interpolation-optimization strategies for multicriteria optimization problems

Soft Computing - A Fusion of Foundations, Methodologies and Applications
Structural topology optimisation using simulated annealing with multiresolution design variables

Finite Elements in Analysis and Design
Particle swarm optimizer, ant colony strategy and harmony search scheme hybridized for optimization of truss structures

Computers and Structures
Fireworks algorithm for optimization

ICSI'10 Proceedings of the First international conference on Advances in Swarm Intelligence - Volume Part I

Performance enhancement of multiobjective evolutionary optimisers for truss design using an approximate gradient

Computers and Structures
Hybridisation of real-code population-based incremental learning and differential evolution for multiobjective design of trusses

Information Sciences: an International Journal

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper proposes a hybrid evolutionary algorithm (EA) dealing with population-based incremental learning (PBIL) and some efficient local search strategies. A simple PBIL using real codes is developed. The evolutionary direction and approximate gradient operators are integrated to the main procedure of PBIL. The method is proposed for single objective global optimization. The search performance of the developed hybrid algorithm for box-constrained optimization is compared with a number of well-established and newly developed evolutionary algorithms and meta-heuristics. It is found that, with the given optimization settings, the proposed hybrid optimizer outperforms the other EAs. The new derivative-free algorithm can maintain outstanding abilities of EAs.