Journal: Evolutionary Computation

Abbreviation

Evol Comput

Publisher

MIT Press

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ISSN

1530-9304
1063-6560

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Publications 1 - 10 of 11
  • Introducing elitist black-box models: When does elitist behavior weaken the performance of evolutionary algorithms?
    Item type: Journal Article
    Doerr, Carola; Lengler, Johannes (2017)
    Evolutionary Computation
  • OneMax Is Not the Easiest Function for Fitness Improvements
    Item type: Journal Article
    Kaufmann, Marc; Larcher, Maxime; Lengler, Johannes; et al. (2025)
    Evolutionary Computation
    We study the (1:s+1) success rule for controlling the population size of the (1,λ)-EA. It was shown by Hevia Fajardo and Sudholt that this parameter control mechanism can run into problems for large s if the fitness landscape is too easy. They conjectured that this problem is worst for the OneMax benchmark, since in some well-established sense OneMax is known to be the easiest fitness landscape. In this paper, we disprove this conjecture. We show that there exist s and ɛ such that the self-adjusting (1,λ)-EA with the (1:s+1)-rule optimizes OneMax efficiently when started with ɛn zero-bits, but does not find the optimum in polynomial time on Dynamic BinVal. Hence, we show that there are landscapes where the problem of the (1:s+1)-rule for controlling the population size of the (1,λ)-EA is more severe than for OneMax. The key insight is that, while OneMax is the easiest function for decreasing the distance to the optimum, it is not the easiest fitness landscape with respect to finding fitness-improving steps.
  • Neuroevolution of a Modular Memory-Augmented Neural Network for Deep Memory Problems
    Item type: Journal Article
    Khadka, Shauharda; Chung, Jen Jen; Tumer, Kagan (2019)
    Evolutionary Computation
  • Computing gap free pareto front approximations with stochastic search algorithms
    Item type: Journal Article
    Schütze, Oliver; Laumanns, Marco; Tantar, Emilia; et al. (2010)
    Evolutionary Computation
  • Reducing the Time Complexity of the Derandomized Evolution Strategy with Covariance Matrix Adaptation (CMA-ES)
    Item type: Journal Article
    Hansen, Nikolaus; Muller, Sibylle D.; Koumoutsakos, Petros (2003)
    Evolutionary Computation
  • An analysis of mutative sigma-self-adaptation on linear fitness functions
    Item type: Journal Article
    Hansen, Nikolaus (2006)
    Evolutionary Computation
  • Covariance matrix adaptation for multi-objective optimization
    Item type: Journal Article
    Igel, Christian; Hansen, Nikolaus; Roth, Stefan (2007)
    Evolutionary Computation
  • HypE: An Algorithm for Fast Hypervolume-Based Many-Objective Optimization
    Item type: Journal Article
    Bader, Johannes; Zitzler, Eckart (2011)
    Evolutionary Computation
    In the field of evolutionary multi-criterion optimization, the hypervolume indicator is the only single set quality measure that is known to be strictly monotonic with regard to Pareto dominance: whenever a Pareto set approximation entirely dominates another one, then the indicator value of the dominant set will also be better. This property is of high interest and relevance for problems involving a large number of objective functions. However, the high computational effort required for hypervolume calculation has so far prevented the full exploitation of this indicator's potential; current hypervolume-based search algorithms are limited to problems with only a few objectives. This paper addresses this issue and proposes a fast search algorithm that uses Monte Carlo simulation to approximate the exact hypervolume values. The main idea is not that the actual indicator values are important, but rather that the rankings of solutions induced by the hypervolume indicator. In detail, we present HypE, a hypervolume estimation algorithm for multi-objective optimization, by which the accuracy of the estimates and the available computing resources can be traded off; thereby, not only do many-objective problems become feasible with hypervolume-based search, but also the runtime can be flexibly adapted. Moreover, we show how the same principle can be used to statistically compare the outcomes of different multi-objective optimizers with respect to the hypervolume—so far, statistical testing has been restricted to scenarios with few objectives. The experimental results indicate that HypE is highly effective for many-objective problems in comparison to existing multi-objective evolutionary algorithms. HypE is available for download at http://www.tik.ee.ethz.ch/sop/download/supplementary/hype/ .
  • Energy Landscapes of Atomic Clusters as Black Box Optimization Benchmarks
    Item type: Journal Article
    Müller, Christian L.; Sbalzarini, Ivo F. (2012)
    Evolutionary Computation
  • Objective Reduction in Evolutionary Multiobjective Optimization: Theory and Applications
    Item type: Journal Article
    Brockhoff, Dimo; Zitzler, Eckart (2009)
    Evolutionary Computation
    Many-objective problems represent a major challenge in the field of evolutionary multiobjective optimization—in terms of search efficiency, computational cost, decision making, visualization, and so on. This leads to various research questions, in particular whether certain objectives can be omitted in order to overcome or at least diminish the difficulties that arise when many, that is, more than three, objective functions are involved. This study addresses this question from different perspectives. First, we investigate how adding or omitting objectives affects the problem characteristics and propose a general notion of conflict between objective sets as a theoretical foundation for objective reduction. Second, we present both exact and heuristic algorithms to systematically reduce the number of objectives, while preserving as much as possible of the dominance structure of the underlying optimization problem. Third, we demonstrate the usefulness of the proposed objective reduction method in the context of both decision making and search for a radar waveform application as well as for well-known test functions.
Publications 1 - 10 of 11