On the complexity of hierarchical problem solving

Jong, Edwin de; Watson, Richard A.; Thierens, Dirk

doi:10.1145/1068009.1068207

Cited by 32 publications

(29 citation statements)

References 11 publications

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“…Recent results are along these lines [16] i.e., hierarchical model building . Additional refinements to the composition model have also been introduced [13]: (1) mutation for supporting population diversity (2) initial population limited to single (non-neutral) genes but allowed to incrementally increase, thus making the hierarchical gene linkage learning more explicit; and, (3) maintenance of a worst case tabu list of poorly performing genomes to bias against revisiting poor states during symbiosis.…”

Section: Symbiogenesis and Genetic Linkage Learningmentioning

confidence: 99%

Symbiogenesis as a Mechanism for Building Complex Adaptive Systems: A Review

Heywood

Lichodzijewski

2010

Applications of Evolutionary Computation

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Abstract. In 1996 Daida et al. reviewed the case for using symbiosis as the basis for evolving complex adaptive systems [6]. Specific observations included the impact of different philosophical views taken by biologists as to what constituted a symbiotic relationship and whether symbiosis represented an operator or a state. The case was made for symbiosis as an operator. Thus, although specific cost benefit characterizations may vary, the underlying process of symbiosis is the same, supporting the operator based perspective. Symbiosis provides an additional mechanism for adaption/ complexification than available under Mendelian genetics with which Evolutionary Computation (EC) is most widely associated. In the following we review the case for symbiosis in EC. In particular, symbiosis appears to represent a much more effective mechanism for automatic hierarchical model building and therefore scaling EC methods to more difficult problem domains than through Mendelian genetics alone.

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Section: Symbiogenesis and Genetic Linkage Learningmentioning

confidence: 99%

Symbiogenesis as a Mechanism for Building Complex Adaptive Systems: A Review

Heywood

Lichodzijewski

2010

Applications of Evolutionary Computation

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“…As stated by de Jong, Watson, and Thierens (2005), 'two variables in a problem are interdependent if the fitness contribution or optimal setting for one variable depends on the setting of the other variable', and such relationship between variables is often referred to as linkage in the GA literature. In order to obtain the full linkage information of a pair of variables, the fitness contribution or optimal setting of these two variables will be examined on all possible settings of the other variables.…”

Section: Linkage and Bbsmentioning

confidence: 99%

Inductive linkage identification on building blocks of different sizes and types

Chen

Chuang

Yuan-wei

2012

International Journal of Systems Science

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The goal of linkage identification is to obtain the dependencies among decision variables. Such information or knowledge can be applied to design crossover operators and/or the encoding schemes in genetic and evolutionary methods. Thus, promising sub-solutions to the problem will be disrupted less likely, and successful convergence may be achieved more likely. To obtain linkage information, a linkage identification technique, called Inductive Linkage Identification (ILI), was proposed recently. ILI was established upon the mechanism of perturbation and the idea of decision tree learning. By constructing a decision tree according to decision variables and fitness difference values, the interdependent variables will be determined by the adopted decision tree learning algorithm. In this article, we aim to acquire a better understanding on the characteristics of ILI, especially its behaviour under problems composed of different-sized and different-type building blocks (BBs) which are not overlapped. Experiments showed that ILI can efficiently handle BBs of different sizes and is insensitive to BB types. Our experimental observations indicate the flexibility and the applicability of ILI on various elementary BB types that are commonly adopted in related experiments.

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“…Prior models have been suggested that investigate the evolution of symbiogenic encapsulation [10][11][12], or abstractions thereof [13]. These models use a variety of mechanisms to determine the suitability of symbiogenic joins, including Pareto dominance [10], context-optimality [13], and maximising reciprocal synergy [12].…”

Section: Introductionmentioning

confidence: 99%

“…These models use a variety of mechanisms to determine the suitability of symbiogenic joins, including Pareto dominance [10], context-optimality [13], and maximising reciprocal synergy [12]. In [14] the evolution of 'observers' provides groupings to coarsegrain an adaptive landscape.…”

Section: Introductionmentioning

confidence: 99%