Genericity in Evolutionary Computation Software Tools: Principles and Case-Study

Gagné, Christian; Parizeau, Marc

doi:10.1142/s021821300600262x

Cited by 100 publications

(85 citation statements)

References 19 publications

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“…The GP engine is provided by Open BEAGLE (Gagné, 2006) an excellent general purpose toolkit for evolutionary computation.…”

Section: Methodsmentioning

confidence: 99%

Evolving textures from high level descriptions

Reynolds

2016

IJART

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Evolutionary texture synthesis is used in a prototype tool intended to assist a designer or artist by automatically discovering collections of candidate textures to fit a given stylistic description. The textures used here are small colour images created by procedural texture synthesis. This paper describes four examples of stylistic description. Each is defined by a handwritten fitness function that rates how well a given texture meets this style. Genetic programming uses the fitness function to evolve programs written in a texture synthesis language. This system automatically generates a catalogue of variations on the given theme. A designer could then visually scan through these textures to pick out ones that seem aesthetically interesting. Their procedural 'genetic' representation would allow textures to be further adjusted by interactive evolution. The procedural representation also allows re-rendering textures at arbitrary pixel resolutions and provides a way to store them in a highly compressed form allowing lossless reconstruction.

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“…The GP engine is provided by Open BEAGLE (Gagné, 2006) an excellent general purpose toolkit for evolutionary computation.…”

Section: Methodsmentioning

confidence: 99%

Evolving textures from high level descriptions

Reynolds

2016

IJART

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“…GP runs were performed on Open BEAGLE software [23], a C++, object-oriented, generic framework for performing Evolutionary Computation. It supports tree-based, stronglytyped genetic programming.…”

Section: Gp Softwarementioning

confidence: 99%

Evolutionary Synthesis of Stochastic Gene Network Models Using Feature-based Search Spaces

Imada

Ross

2011

New Gener. Comput.

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A feature-based fitness function is applied in a genetic programming system to synthesize stochastic gene regulatory network models whose behaviour is defined by a time course of protein expression levels. Typically, when targeting time series data, the fitness function is based on a sum-of-errors involving the values of the fluctuating signal. While this approach is successful in many instances, its performance can deteriorate in the presence of noise. This thesis explores a fitness measure determined from a set of statistical features characterizing the time series' sequence of values, rather than the actual values themselves.Through a series of experiments involving symbolic regression with added noise and gene regulatory network models based on the stochastic π-calculus, it is shown to successfully target oscillating and non-oscillating signals. This practical and versatile fitness function offers an alternate approach, worthy of consideration for use in algorithms that evaluate noisy or stochastic behaviour.

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“…However, very few are able to handle MOPs, even if some of them provide components for a few particular EMO strategies, such as ECJ [1], JavaEVA [42] or Open BEAGLE [20]. Table 5.1 gives a non-exhaustive comparison between a number of existing software frameworks for multi-objective metaheuristics, including jMetal [17], the MOEA toolbox for Matlab [45], MOMHLib++ [2], PISA [7] .…”

Section: Existing Software Framework For Evolutionary Multi-objectivmentioning

confidence: 99%

ParadisEO-MOEO: A Software Framework for Evolutionary Multi-Objective Optimization

Liefooghe

Jourdan

Legrand

et al. 2010

Advances in Multi-Objective Nature Inspired Computing

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This chapter presents ParadisEO-MOEO, a white-box object-oriented software framework dedicated to the flexible design of metaheuristics for multi-objective optimization. This paradigm-free software proposes a unified view for major evolutionary multi-objective metaheuristics. It embeds some features and techniques for multi-objective resolution and aims to provide a set of classes allowing to ease and speed up the development of computationally efficient programs. It is based on a clear conceptual distinction between the solution methods and the problems they are intended to solve. This separation confers a maximum design and code reuse. This general-purpose framework provides a broad range of fitness assignment strategies, the most common diversity preservation mechanisms, some elitistrelated features as well as statistical tools. Furthermore, a number of state-of-the-art search methods, including NSGA-II, SPEA2 and IBEA, have been implemented in a user-friendly way, based on the fine-grained ParadisEO-MOEO components

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Genericity in Evolutionary Computation Software Tools: Principles and Case-Study

Cited by 100 publications

References 19 publications

Evolving textures from high level descriptions

Evolving textures from high level descriptions

Evolutionary Synthesis of Stochastic Gene Network Models Using Feature-based Search Spaces

ParadisEO-MOEO: A Software Framework for Evolutionary Multi-Objective Optimization

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