FOGA

Analyzing the computational complexity of evolutionary algorithms has become an accepted and important branch in evolutionary computation theory. This is usually done by analyzing the (expected) optimization time measured by means of the number of ...

We study the behaviour of the (1,λ)-ES for a linear problem with a single linear constraint. The algorithm produces offspring until λ feasible candidate solutions have been generated and selects the best of those as the next generation's parent. ...

Genetic Programming parity with only XOR is not elementary. GP parity can be represented as the sum of k/2+1 elementary landscapes. Statistics, including fitness distance correlation (FDC), of Parity's fitness landscape are calculated. Using Walsh ...

Co-optimization problems involve one or more search spaces and a means of assessing interactions between entities in these spaces. Assessing a potential solution requires aggregating in some way the outcomes of a very large or infinite number of such ...

We present lower and upper bounds on runtimes for expensive noisy optimization problems. Runtimes are expressed in terms of number of fitness evaluations. Fitnesses considered are monotonic transformations of the sphere function. The analysis focuses on ...

Analyzing the computational complexity of evolutionary algorithms (EAs) for binary search spaces has significantly informed our understanding of EAs in general. With this paper, we start the computational complexity analysis of genetic programming (GP). ...

It was recently proven that sets of points maximizing the hypervolume indicator do not give a good multiplicative approximation of the Pareto front. We introduce a new "logarithmic hypervolume indicator" and prove that it achieves a close-to-optimal ...

The distribution of fitness values across a set of states sharply influences the dynamics of evolutionary processes and heuristic search in combinatorial optimization. In this paper we present a method for approximating the distribution of fitness ...

This paper is concerned with the question to which extent a change in the selective pressure might improve the runtime of an optimization algorithm considerably. The subject of this examination is the class of symmetric functions, i.e. OneMax with a ...

In this work, we investigate a (1+1) Evolutionary Algorithm for optimizing functions over the space {0,...,r} ⁿ, where r is a positive integer. We show that for linear functions over {0,1,2}ⁿ, the expected runtime time of this algorithm is O(n log n). ...

This paper presents a refined single parent evolution strategy that is derandomized with mirrored sampling and/or uses sequential selection. The paper analyzes some of the elitist variants of this algorithm. We prove, on spherical functions with finite ...

The Markov chain Monte Carlo paradigm has developed powerful and elegant techniques for estimating the time until a Markov chain approaches a stationary distribution. This time is known as mixing time. We introduce the reader into mixing time ...

Geometric crossover is a formal class of crossovers which includes many well-known recombination operators across representations. In this paper, we present a general result showing that all evolutionary algorithms using geometric crossover with no ...

We extend the work of Lehre and Witt (GECCO 2010) on the unbiased black-box model by considering higher arity variation operators. In particular, we show that already for binary operators the black-box complexity of LeadingOnes drops from Θ(n²) for ...

Many practical optimisation problems allow candidate solutions of varying lengths, and where the length of the optimal solution is thereby a priori unknown. We suggest that non-uniform mutation rates can be beneficial when solving such problems. In ...

We present two adaptive schemes for dynamically choosing the number of parallel instances in parallel evolutionary algorithms. This includes the choice of the offspring population size in a (1+λ) EA as a special case. Our schemes are parameterless and ...

The Vose dynamical system model of the simple genetic algorithm models the behavior of this algorithm for large population sizes and is the basis of the exact Markov chain model. Populations consisting of multiple copies of one individual correspond to ...

With this paper, we contribute to the understanding of ant colony optimization (ACO) algorithms by formally analyzing their runtime behavior. We study simple MAX-MIN ant systems on the class of linear pseudo-Boolean functions defined on binary strings ...

Customizing and evolutionary algorithm (EA) for a new or unusual problem can seem relatively simple as long as one can devise an appropriate representation and reproductive operators to modify it. Unfortunately getting a customized EA to produce high ...

In this paper we present a geometrical framework for the analysis of Estimation of Distribution Algorithms (EDAs) based on the exponential family. From a theoretical point of view, an EDA can be modeled as a sequence of densities in a statistical model ...

Convergence rate analyses of evolutionary multi-objective optimization algorithms in continuous search space are yet rare. First results have been obtained for simple algorithms. Here, we provide concrete results of convergence rates for a state-of-the-...