Coevolutionary algorithms

Paredis, Jan

doi:10.1887/0750306653/b840c23

Cited by 7 publications

(8 citation statements)

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“…Biological co-evolution has been the inspiration for a class of computational algorithms called co-evolutionary computing (Paredis, 1998). Co-evolutionary design, as introduced in Maher (1994), is an approach to design problem solving in which the requirements and solutions of design evolve separately and affect each other.…”

Section: Introductionmentioning

confidence: 99%

Co-evolution as a computational and cognitive model of design

Maher

Tang²

2003

Res Eng Design

125

107

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Abstract. Co-evolutionary design has been developed as a computational model that assumes two parallel search spaces: the problem space and the solution space. The design process iteratively searches each space using the other space as the basis for a fitness function when evaluating the alternatives. Co-evolutionary design can also be developed as a cognitive model of design by characterizing the way in which designers iteratively search for a design solution, making revisions to the problem specification. This paper presents the computational model of co-evolutionary design and then describes a protocol study of human designers looking for evidence of co-evolution of problem specifications and design solutions. The study shows that co-evolutionary design is a good cognitive model of design, and highlights the similarities and differences between the computational model and the cognitive model. The results show that the two kinds of co-evolutionary design complement each other, having strengths in different aspects of the design process.Keywords: co-evolutionary design, computational model of design, cognitive model of design, protocol studies

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Section: Introductionmentioning

confidence: 99%

Co-evolution as a computational and cognitive model of design

Maher

Tang²

2003

Res Eng Design

125

107

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“…Coevolution Coevolutionary algorithms are EAs in which the fitness of a particular individual depends on other individuals, which are also evolved [150]. These approaches are closer to what happens in the living world, where the selection process depends on the ecological niche of a particular species, including other evolving species (predators or prey, for instance).…”

Section: Mono-objective Eamentioning

confidence: 99%

Beyond black-box optimization: a review of selective pressures for evolutionary robotics

Doncieux

Mouret

2014

Evol. Intel.

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Evolutionary robotics is often viewed as the application of a family of black-box optimization algorithms -evolutionary algorithms -to the design of robots, or parts of robots. When considering evolutionary robotics as black-box optimization, the selective pressure is mainly driven by a user-defined, black-box fitness function, and a domain-independent selection procedure. However, most evolutionary robotics experiments face similar challenges in similar setups: the selective pressure, and, in particular, the fitness function, is not a pure user-defined black box. The present review shows that, because evolutionary robotics experiments share common features, selective pressures for evolutionary robotics are a subject of research on their own. The literature has been split into two categories: goal refiners, aimed at changing the definition of a good solution, and process helpers, designed to help the search process. Two subcategories are further considered: task-specific approaches, which require knowledge on how to solve the task and taskagnostic ones, which do not need it. Besides highlighting the diversity of the approaches and their respective goals, the present review shows that many task-agnostic process helpers have been proposed during the last years, thus bringing us closer to the goal of a fully automated robot behavior design process.

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“…He also examined the effect of different pairing strategies [35], with mixed results, although the NKC systems he investigated used fixed interaction patterns. Parker and Blumenthal's "Punctuated Anytime Learning with samples" [36] is a recent approach to this problem of deciding how to pair members of different populations using periodic sampling to estimate fitness There has also been a large body of research into competitive co-evolution (see [37] for an overview). Here the fitnesses assigned to the two populations are directly related to how well individuals perform "against" the other populationwhat has been termed "predator-prey" interactions.…”

Section: B Self-adaptation and Co-evolutionmentioning

confidence: 99%