FOGA

Randomized algorithms are often enjoyed for their simplicity, but the hash functions employed to yield the desired probabilistic guarantees are often too complicated to be practical. Here we survey recent results on how simple hashing schemes based on ...

There has been renewed interest in modelling the behaviour of evolutionary algorithms (EAs) by more traditional mathematical objects, such as ordinary differential equations or Markov chains. The advantage is that the analysis becomes greatly ...

We present the analysis of a discrete particle swarm optimization (PSO) algorithm that works on a significantly large class of discrete optimization problems. Assuming a black-box setting, we prove upper and lower bounds on the expected number of ...

Noise is pervasive in real-world optimization, but there is still little understanding of the interplay between the operators of randomized search heuristics and explicit noise-handling techniques, such as statistical resampling. In this paper, we ...

We consider the weighted minimum vertex cover problem and investigate how its dual formulation can be exploited to design evolutionary algorithms that provably obtain a 2-approximation. Investigating multi-valued representations, we show that variants ...

Linear functions have gained a lot of attention in the area of run time analysis of evolutionary computation methods and the corresponding analyses have provided many effective tools for analyzing more complex problems. In this paper, we consider the ...

Clearing is a niching method inspired by the principle of assigning the available resources among a subpopulation to a single individual. The clearing procedure supplies these resources only to the best individual of each subpopulation: the winner. So ...

The Univariate Marginal Distribution Algorithm (UMDA), a popular estimation of distribution algorithm, is studied from a run time perspective. On the classical OneMax benchmark function, a lower bound of Ω(μ√n + n log n), where μ is the population size, ...

Factored Evolutionary Algorithms (FEA) have been found to be an effective way to optimize single objective functions by partitioning the variables in the function into overlapping subpopulations, or factors. While there exist several works empirically ...

Hypervolume subset selection is to find a pre-specified number of solutions for hypervolume maximization. The optimal distribution of solutions on the Pareto front has been theoretically studied for two-objective problems in the literature. In this ...

We investigate evolution strategies with weighted recombination on general convex quadratic functions. We derive the asymptotic quality gain in the limit of the dimension to infinity, and derive the optimal recombination weights and the optimal step-...

We explore the ability of Evolution Strategies (ESs) to statistically learn the local landscape. Specifically, we consider ESs operating only with isotropic Gaussian mutations near the optimum and investigate the covariance matrix when constructed out ...

We present a comparison of step size adaptation methods for evolution strategies, covering recent developments in the field. Following recent work by Hansen et al. we formulate a concise list of performance criteria: a) fast convergence of the mean, b) ...

We analyze linear convergence of an evolution strategy for constrained optimization with an augmented Lagrangian constraint handling approach. We study the case of multiple active linear constraints and use a Markov chain approach---used to analyze ...