Evolving Objects: A General Purpose Evolutionary Computation Library

Keijzer, Maarten; Guervós, Juan Julián Merelo; Romero, Gustavo; Schoenauer, Marc

doi:10.1007/3-540-46033-0_19

Cited by 126 publications

(109 citation statements)

References 26 publications

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“…EvoRobot was ported on Linux platform using OpenGL graphical library, and interfaced with the EO library [9]. It is hence now possible to use all features of EO in the context of Evolutionary Robotics, e.g.…”

Section: Methodsmentioning

confidence: 99%

Evolving Symbolic Controllers

Godzik

Schoenauer

Sebag

2003

Lecture Notes in Computer Science

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Abstract. The idea of symbolic controllers tries to bridge the gap between the top-down manual design of the controller architecture, as advocated in Brooks' subsumption architecture, and the bottom-up designerfree approach that is now standard within the Evolutionary Robotics community. The designer provides a set of elementary behavior, and evolution is given the goal of assembling them to solve complex tasks. Two experiments are presented, demonstrating the efficiency and showing the recursiveness of this approach. In particular, the sensitivity with respect to the proposed elementary behaviors, and the robustness w.r.t. generalization of the resulting controllers are studied in detail.

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Section: Methodsmentioning

confidence: 99%

Evolving Symbolic Controllers

Godzik

Schoenauer

Sebag

2003

Lecture Notes in Computer Science

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“…Based on EO (Evolving Objects) 3 [30], this template-based, ANSI-C++ compliant computation library is portable across both Unix-like and Windows systems. Moreover, it tends to be used both by non-specialists and optimization experts.…”

Section: Paradiseo and Paradiseo-moeomentioning

confidence: 99%

“…This separation confers a maximum code and design reuse to the user. The first module, ParadisEO-EO [30], provides a broad range of components for the development of population-based metaheuristics, including evolutionary algorithms and particle swarm optimization techniques. Second, ParadisEO-MO [8] contains a set of tools for single-solution based metaheuristics, i.e.…”

Section: Paradiseo and Paradiseo-moeomentioning

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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“…As a result, an evolutionary algorithm specified in the EASEA language (or specified and saved by GUIDE with an EASEA syntax) can be compiled in a C++ source file using the GALib evolutionary library [12], or the EO [9,1] fully templatised object oriented library; or of course in Java source files using the JEO library of the DREAM.…”

Section: Problem-dependent Componentsmentioning

confidence: 99%

A Framework for Distributed Evolutionary Algorithms

Arenas

Collet

Eiben

et al. 2002

Lecture Notes in Computer Science

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Abstract. This paper describes the recently released DREAM (Distributed Resource Evolutionary Algorithm Machine) framework for the automatic distribution of evolutionary algorithm (EA) processing through a virtual machine built from large numbers of individual machines linked by standard Internet protocols. The framework allows five different user entry points which depend on the knowledge and requirements of the user. At the highest level, users may specify and run distributed EAs simply by manipulating graphical displays. At the lowest level the framework turns becomes a P2P (Peer to Peer) mobile agent system, that may be used for the automatic distribution of a class of processes including, but not limited to, EAs.

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Evolving Objects: A General Purpose Evolutionary Computation Library

Cited by 126 publications

References 26 publications

Evolving Symbolic Controllers

Evolving Symbolic Controllers

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

A Framework for Distributed Evolutionary Algorithms

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