Search Results

With apparently all research on estimation-of-distribution algorithms (EDAs) concentrated on pseudo-Boolean optimization and permutation problems, we undertake the first steps towards using EDAs for problems in which the decision variables can take ...

Estimation-of-distribution algorithms (EDAs) are optimization algorithms that learn a distribution from which good solutions can be sampled easily. A key parameter of most EDAs is the sample size (population size). Too small values lead to the undesired ...

Inspired from the optimal mixing in the linkage tree gene-pool optimal mixing evolutionary algorithm, the dependent structure matrix genetic algorithm II (DSMGA-II) is one of the state-of-the-art model-building genetic algorithms. It obtains the patterns ...

The compact genetic algorithm (cGA) is a non-elitist estimation of distribution algorithm which has shown to be able to deal with difficult multimodal fitness landscapes that are hard to solve by elitist algorithms. In this paper, we investigate the cGA ...

The benefits of using crossover in crossing fitness gaps have been studied extensively in evolutionary computation. Recent runtime results show that majority-vote crossover is particularly efficient at optimizing the well-known Jump benchmark function ...

Finding a large set of optima in a multimodal optimization landscape is a challenging task. Classical population-based evolutionary algorithms (EAs) typically converge only to a single solution. While this can be counteracted by applying niching ...

One of the key difficulties in using estimation-of-distribution algorithms is choosing the population sizes appropriately: Too small values lead to genetic drift, which can cause enormous difficulties. In the regime with no genetic drift, however, often ...

We study the intricate dynamics of the Compact Genetic Algorithm (cGA) on OneMax, and how its performance depends on the step size 1/K, that determines how quickly decisions about promising bit values are fixed in the probabilistic model. It is known ...

Learning and exploiting problem structure is one of the key challenges in optimization. This is especially important for black-box optimization BBO where prior structural knowledge of a problem is not available. Existing model-based Evolutionary ...

The recently introduced Gene-pool Optimal Mixing Evolutionary Algorithm (GOMEA) family, which includes the Linkage Tree Genetic Algorithm (LTGA), has been shown to scale excellently on a variety of discrete, Cartesian-space, optimization problems. This ...

We provide a rigorous runtime analysis concerning the update strength, a vital parameter in probabilistic model-building GAs such as the step size 1/K in the compact Genetic Algorithm (cGA) and the evaporation factor ρ in ACO. While a large update ...

The Covariance Matrix Adaptation Evolutionary Strategy (CMA-ES) is a well-known, state-of-the-art optimization algorithm for single-objective real-valued problems, especially in black-box settings. Although several extensions of CMA-ES to multi-...

Hierarchical problems represent an important class of nearly decomposable problems. The concept of near decomposability is central to the study of complex systems. When little a priori information is available, a black box problem solver is needed to ...

Genepool Optimal Mixing Evolutionary Algorithms (GOMEAs) were recently proposed as a new way of designing linkage-friendly, efficiently-scalable evolutionary algorithms (EAs). GOMEAs combine the building of linkage models with an intensive, greedy ...

Recently, the Linkage Tree Genetic Algorithm (LTGA) was introduced as one of the latest developments in a line of EA research that studies building models to capture and exploit linkage information between problem variables. LTGA was reported to exhibit ...

The linkage tree genetic algorithm (LTGA) learns, each generation, a linkage model by building a hierarchical cluster tree. The LTGA is an instance of the more general gene-pool optimal mixing evolutionary algorithm (GOMEA) that uses a family of subsets ...

Estimation-of-Distribution Algorithms (EDAs) build and use probabilistic models during optimization in order to automatically discover and use an optimization problems' structure. This is especially useful for black-box optimization where no assumptions ...

The inclusion of local search (LS) techniques in evolutionary algorithms (EAs) is known to be very important in order to obtain competitive results on combinatorial and real-world optimization problems. Often however, an important source of the added ...