31,291 research outputs found
Sub-structural Niching in Estimation of Distribution Algorithms
We propose a sub-structural niching method that fully exploits the problem
decomposition capability of linkage-learning methods such as the estimation of
distribution algorithms and concentrate on maintaining diversity at the
sub-structural level. The proposed method consists of three key components: (1)
Problem decomposition and sub-structure identification, (2) sub-structure
fitness estimation, and (3) sub-structural niche preservation. The
sub-structural niching method is compared to restricted tournament selection
(RTS)--a niching method used in hierarchical Bayesian optimization
algorithm--with special emphasis on sustained preservation of multiple global
solutions of a class of boundedly-difficult, additively-separable multimodal
problems. The results show that sub-structural niching successfully maintains
multiple global optima over large number of generations and does so with
significantly less population than RTS. Additionally, the market share of each
of the niche is much closer to the expected level in sub-structural niching
when compared to RTS
Exploiting linkage information and problem-specific knowledge in evolutionary distribution network expansion planning
This article tackles the Distribution Network Expansion Planning (DNEP) problem
that has to be solved by distribution network operators to decide which, where,
and/or when enhancements to electricity networks should be introd uced to
satisfy the future power demands. Because of many real-world details involved,
the structure of the problem is not exploited easily using mathematical
programming techniques, for which reason we consider solving this problem with
evolutionary algorithms (EAs). We compare three types of EAs for optimizing
expansion plans : the classic genetic algorithm (GA), the
estimation-of-distribution algorith m (EDA), and the Gene-pool Optimal Mixing
Evolutionary Algorithm (GOMEA). Not fully k nowing the structure of the problem,
we study the effect of linkage learning through the use of three linkage models:
univariate, marginal product, and linkage tree. We furthermore experiment with
the impact of incorporating different levels of proble m-specific knowledge in
the variation operators. Experiments show that the use of problem-specific
variation operators is far more important for the classic GA to find
high-quality solutions. In all EAs, the marginal product model and its linkage
learning pro cedure have difficulty in capturing and exploiting the DNEP problem
structure. GOMEA, especially when combined with the linkage tree structure, is
found to have the most robust performance by far
Learning and Searching Pseudo-Boolean Surrogate Functions from Small Samples
When searching for input configurations that optimise the output of a system, it can be useful to build a statistical model of the system being optimised. This is done in approaches such as surrogate model-based optimisation, estimation of distribution algorithms and linkage learning algorithms. This paper presents a method for modelling pseudo-Boolean fitness functions using Walsh bases and an algorithm designed to discover the non-zero coefficients while attempting to minimise the number of fitness function evaluations required. The resulting models reveal linkage structure that can be used to guide a search of the model efficiently. It presents experimental results solving benchmark problems in fewer fitness function evaluations than those reported in the literature for other search methods such as EDAs and linkage learners
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