An Introduction to Genetic Algorithms
An Introduction to Genetic Algorithms
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
On the importance of diversity maintenance in estimation of distribution algorithms
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Soft Computing - A Fusion of Foundations, Methodologies and Applications
A Convergence Proof for the Population Based Incremental Learning Algorithm
ICTAI '05 Proceedings of the 17th IEEE International Conference on Tools with Artificial Intelligence
Addressing sampling errors and diversity loss in UMDA
Proceedings of the 9th annual conference on Genetic and evolutionary computation
A Population-Based Incremental Learning Algorithm with Elitist Strategy
ICNC '07 Proceedings of the Third International Conference on Natural Computation - Volume 03
Optimizing Curriculum Scheduling Problem Using Population Based Incremental Learning Algorithm
DMAMH '07 Proceedings of the Second Workshop on Digital Media and its Application in Museum & Heritage
Radio Network Design Using Population-Based Incremental Learning and Grid Computing with BOINC
Proceedings of the 2007 EvoWorkshops 2007 on EvoCoMnet, EvoFIN, EvoIASP,EvoINTERACTION, EvoMUSART, EvoSTOC and EvoTransLog: Applications of Evolutionary Computing
Diversity loss in general estimation of distribution algorithms
PPSN'06 Proceedings of the 9th international conference on Parallel Problem Solving from Nature
Center-based sampling for population-based algorithms
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Ectropy of diversity measures for populations in Euclidean space
Information Sciences: an International Journal
Enhancing particle swarm optimization using generalized opposition-based learning
Information Sciences: an International Journal
An intuitive distance-based explanation of opposition-based sampling
Applied Soft Computing
Accelerated biogeography-based optimization with neighborhood search for optimization
Applied Soft Computing
Survey A review of opposition-based learning from 2005 to 2012
Engineering Applications of Artificial Intelligence
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In this paper we propose a new probability update rule and sampling procedure for population-based incremental learning. These proposed methods are based on the concept of opposition as a means for controlling the amount of diversity within a given sample population. We prove that under this scheme we are able to asymptotically guarantee a higher diversity, which allows for a greater exploration of the search space. The presented probabilistic algorithm is specifically for applications in the binary domain. The benchmark data used for the experiments are commonly used deceptive and attractor basin functions as well as 10 common travelling salesman problem instances. Our experimental results focus on the effect of parameters and problem size on the accuracy of the algorithm as well as on a comparison to traditional population-based incremental learning. We show that the new algorithm is able to effectively utilize the increased diversity of opposition which leads to significantly improved results over traditional population-based incremental learning.