Journal: IEEE Transactions on Evolutionary Computation

Abbreviation

IEEE trans. evol. comput.

Publisher

IEEE

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ISSN

1089-778X
1941-0026

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Publications1 - 10 of 14
  • Systematic integration of parameterized local search into evolutionary algorithms
    Item type: Journal Article
    Bambha, N.K.; Bhattacharyya, S.S.; Teich, J.; et al. (2004)
    IEEE Transactions on Evolutionary Computation
  • Multiobjective groundwater management using evolutionary algorithms
    Item type: Journal Article
    Siegfried, Tobias; Bleuler, Stefan; Laumanns, Marco; et al. (2009)
    IEEE Transactions on Evolutionary Computation
  • Running time analysis of multiobjective evolutionary algorithms on Pseudo-Boolean functions
    Item type: Journal Article
    Laumanns, Marco; Thiele, Lothar; Zitzler, Eckart (2004)
    IEEE Transactions on Evolutionary Computation
  • A Method for Handling Uncertainty in Evolutionary Optimization With an Application to Feedback Control of Combustion
    Item type: Journal Article
    Hansen, Nikolaus; Niederberger, André S.P.; Guzzella, Lino; et al. (2009)
    IEEE Transactions on Evolutionary Computation
  • On the Effects of Adding Objectives to Plateau Functions
    Item type: Journal Article
    Brockhoff, Dimo; Friedrich, Tobias; Hebbinghaus, Nils; et al. (2009)
    IEEE Transactions on Evolutionary Computation
  • A General Dichotomy of Evolutionary Algorithms on Monotone Functions
    Item type: Journal Article
    Lengler, Johannes (2020)
    IEEE Transactions on Evolutionary Computation
    It is known that the (1 + 1)-EA with mutation rate c/n optimizes every monotone function efficiently if c<1 , and needs exponential time on some monotone functions (HotTopic functions) if c≥2.2 . We study the same question for a large variety of algorithms, particularly for the (1+λ) -EA, (μ+1) -EA, (μ+1) -GA, their “fast” counterparts, and for the (1+(λ,λ)) -GA. We find that all considered mutation-based algorithms show a similar dichotomy for HotTopic functions, or even for all monotone functions. For the (1+(λ,λ)) -GA, this dichotomy is in the parameter cγ , which is the expected number of bit flips in an individual after mutation and crossover, neglecting selection. For the fast algorithms, the dichotomy is in m2/m1 , where m1 and m2 are the first and second falling moment of the number of bit flips. Surprisingly, the range of efficient parameters is not affected by either population size μ nor by the offspring population size λ . The picture changes completely if crossover is allowed. The genetic algorithms (μ+1) -GA and (μ+1) -fGA are efficient for arbitrary mutations strengths if μ is large enough.
  • Optimization based on bacterial chemotaxis
    Item type: Journal Article
    Muller, S.D.; Marchetto, J.; Airaghi, S.; et al. (2002)
    IEEE Transactions on Evolutionary Computation
  • Convergence of Hypervolume-Based Archiving Algorithms
    Item type: Journal Article
    Bringmann, Karl; Friedrich, Tobias (2014)
    IEEE Transactions on Evolutionary Computation
  • Evolutionary multiobjective optimization for base station transmitter placement with frequency assignment
    Item type: Journal Article
    Weicker, N.; Szabó, Gábor; Weicker, K.; et al. (2003)
    IEEE Transactions on Evolutionary Computation
  • On Set-Based Multiobjective Optimization
    Item type: Journal Article
    Zitzler, Eckart; Thiele, Lothar; Bader, Johannes (2010)
    IEEE Transactions on Evolutionary Computation
    Assuming that evolutionary multiobjective optimization (EMO) mainly deals with set problems, one can identify three core questions in this area of research: 1) how to formalize what type of Pareto set approximation is sought; 2) how to use this information within an algorithm to efficiently search for a good Pareto set approximation; and 3) how to compare the Pareto set approximations generated by different optimizers with respect to the formalized optimization goal. There is a vast amount of studies addressing these issues from different angles, but so far only a few studies can be found that consider all questions under one roof. This paper is an attempt to summarize recent developments in the EMO field within a unifying theory of set-based multiobjective search. It discusses how preference relations on sets can be formally defined, gives examples for selected user preferences, and proposes a general preference-independent hill climber for multiobjective optimization with theoretical convergence properties. Furthermore, it shows how to use set preference relations for statistical performance assessment and provides corresponding experimental results. The proposed methodology brings together preference articulation, algorithm design, and performance assessment under one framework and thereby opens up a new perspective on EMO.
Publications1 - 10 of 14