The effects of interaction frequency on the optimization performance of cooperative coevolution

Popovici, Elena; Jong, Kenneth De

doi:10.1145/1143997.1144061

Cited by 18 publications

(8 citation statements)

References 11 publications

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“…Thus, practitioners are free to design their patterns, either sequential or parallel, as introduced previously, when evolving species. Some more complex patterns can also be applied, such as a sequential pattern with various interaction frequencies (Popovici and De Jong 2006 This paper is organized as follows. Section 2 introduces some existing collaboration models and discusses some important issues addressed by different models.…”

Section: New Architecture Of Cooperative Coevolutionmentioning

confidence: 99%

Reference sharing: a new collaboration model for cooperative coevolution

Shi

Gao

2017

J Heuristics

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Cooperative coevolutionary algorithms have been a popular and effective learning approach to solve optimization problems through problem decomposition. However, their performance is highly sensitive to the degree of problem separability. Different collaboration mechanisms usually have to be chosen for particular problems. In the paper, we aim to design a collaboration model that can be successfully applied to a wide range of problems. We present a novel collaboration mechanism that offers this type of potential, along with a new sorting strategy for individuals that are assigned multiple fitness values. Furthermore, we demonstrate and analyze our algorithm through comparison studies with other popular cooperative coevolutionary models on a suite of standard function optimization problems.

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Section: New Architecture Of Cooperative Coevolutionmentioning

confidence: 99%

Reference sharing: a new collaboration model for cooperative coevolution

Shi

Gao

2017

J Heuristics

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“…Individuals of a subpopulation are evaluated by aggregation with individuals of other subpopulations. Multi-species cooperative co-evolution has been applied to various problems [43,55,54,22,36,66], including learning problems [8], and some theoretical analyses have been recently proposed, see [48,10,52], or [65] for an analysis considering a relationship between cooperative co-evolution and evolutionary game theory.…”

Section: Introductionmentioning

confidence: 99%

Cooperative Coevolution for Agrifood Process Modeling

Barrière

Lutton

Wuillemin

et al. 2013

EVOLVE- A Bridge Between Probability, Set Oriented Numerics and Evolutionary Computation

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On the contrary to classical schemes of evolutionary optimisations algorithms, single population Cooperative Co-evolution techniques (CCEAs, also called "Parisian" approaches) make it possible to represent the evolved solution as an aggregation of several individuals (or even as a whole population). In other words, each individual represents only a part of the solution. This scheme allows simulating the principles of Darwinian evolution in a more economic way, which results in gain in robustness and efficiency. The counterpart however is a more complex design phase. In this chapter, we detail the design of efficient CCEAs schemes on two applications related to the modeling of an industrial agri-food process. The experiments correspond to complex optimisations encountered in the modeling of a Camembert-cheese ripening process. Two problems are considered:• A deterministic modeling problem, phase prediction, for which a search for a closed form tree expression is performed using genetic programming (GP).• A Bayesian network structure estimation problem. The novelty of the proposed approach is based on the use of a two step process based on an intermediate representation called independence model. The search for an independence model is formulated as a complex optimisation problem, for which the CCEA scheme is particularly well suited. A Bayesian network is finally deduced using a deterministic algorithm, as a representative of the equivalence class figured by the independence model.

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“…These approaches can be shared into two main categories: heterogeneous co-evolution that happens between a fixed number of separate populations [7,20,21], and homogeneous co-evolution, that occurs within a single population [17,27,29].…”

Section: Introductionmentioning

confidence: 99%

A benchmark for cooperative coevolution

2012

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Cooperative co-evolution algorithms (CCEA) are a thriving sub-field of evolutionary computation. This class of algorithms makes it possible to exploit more efficiently the artificial Darwinist scheme, as soon as an optimisation problem can be turned into a co-evolution of interdependent sub-parts of the searched solution. Testing the efficiency of new CCEA concepts, however, it is not straightforward: while there is a rich literature of benchmarks for more traditional evolutionary techniques, the same does not hold true for this relatively new paradigm. We present a benchmark problem designed to study the behavior and performance of CCEAs, modeling a search for the optimal placement of a set of lamps inside a room. The relative complexity of the problem can be adjusted by operating on a single parameter. The fitness function is a trade-off between conflicting objectives, so the performance of an algorithm can be examined by making use of different metrics. We show how three different cooperative strategies, Parisian Evolution (PE), Group Evolution (GE) and Allopatric Group Evolution (AGE), can be applied to the problem. Using a Classical Evolution (CE) approach as comparison, we analyse the behavior of each algorithm in detail, with respect to the size of the problem capito.

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The effects of interaction frequency on the optimization performance of cooperative coevolution

Cited by 18 publications

References 11 publications

Reference sharing: a new collaboration model for cooperative coevolution

Reference sharing: a new collaboration model for cooperative coevolution

Cooperative Coevolution for Agrifood Process Modeling

A benchmark for cooperative coevolution

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