Genetic and Evolutionary Algorithm Toolbox for Matlab

Pohlheim, Hartmut

doi:10.1007/978-3-642-57137-4_6

Cited by 98 publications

(128 citation statements)

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“…The GEATbx [7] is the optimization component used by the system. The authors applied the standard settings for this class of problem (namely, 6 subpopulations with 50 individuals each, linear ranking, a selection pressure of 1.7, migration and competition between subpopulations).…”

Section: Methodsmentioning

confidence: 99%

“…Using EA for a search problem it is necessary to define the search space and the objective function (fitness). The algorithms are implemented in a widely used tool box [7]. It consists of a large set of operators e.g.…”

Section: Overview Of Evolutionary Testing (Et)mentioning

confidence: 99%

“…For nearly every realistic system test we receive multiple objective values. In order to assess the quality of all objectives tested we employ multi-objective ranking as supported by the GEATbx [7]. This includes Pareto-ranking, goal attainment, fitness sharing and an archive of previously found solutions.…”

Section: Evaluation Of Output Sequences and Objective Functionmentioning

confidence: 99%

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Structural and Functional Sequence Test of Dynamic and State-Based Software with Evolutionary Algorithms

Baresel

Pohlheim

Sadeghipour³

2003

Genetic and Evolutionary Computation — GECCO 2003

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Abstract. Evolutionary Testing (ET) has been shown to be very successful for testing real world applications [10]. The original ET approach focuses on searching for a high coverage of the test object by generating separate inputs for single function calls. We have identified a large set of real world application for which this approach does not perform well because only sequential calls of the tested function can reach a high structural coverage (white box test) or can check functional behavior (black box tests). Especially, control software which is responsible for controlling and constraining a system cannot be tested successfully with ET. Such software is characterized by storing internal data during a sequence of calls. In this paper we present the Evolutionary Sequence Testing approach for white box and black box tests. For automatic sequence testing, a fitness function for the application of ET will be introduced, which allows the optimization of input sequences that reach a high coverage of the software under test. The authors also present a new compact description for the generation of real-world input sequences for functional testing. A set of objective functions to evaluate the test output of systems under test have been developed. These approaches are currently used for the structural and safety testing of car control systems.

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Section: Methodsmentioning

confidence: 99%

Section: Overview Of Evolutionary Testing (Et)mentioning

confidence: 99%

Section: Evaluation Of Output Sequences and Objective Functionmentioning

confidence: 99%

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Structural and Functional Sequence Test of Dynamic and State-Based Software with Evolutionary Algorithms

Baresel

Pohlheim

Sadeghipour³

2003

Genetic and Evolutionary Computation — GECCO 2003

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“…Eldred et al, 2006;Izzo, 2012;Matott, 2005;Pohlheim, 2007;Vesselinov and Harp, 2012). Various implementation approaches have been taken, including: binaries written in C/C++ or FORTRAN (e.g.…”

Section: Current Statusmentioning

confidence: 99%

Evolutionary algorithms and other metaheuristics in water resources: Current status, research challenges and future directions

Maier

Kapelan

Kasprzyk

et al. 2014

Environmental Modelling & Software

531

317

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In all cases accepted manuscripts should:  link to the formal publication via its DOI  bear a CC-BY-NC-ND license -this is easy to do, click here to find out how  if aggregated with other manuscripts, for example in a repository or other site, be shared in alignment with our hosting policy  not be added to or enhanced in any way to appear more like, or to substitute for, the published journal article AbstractThe development and application of evolutionary algorithms (EAs) and other metaheuristics for the optimisation of water resources systems has been an active research field for over two decades. Research to date has emphasized algorithmic improvements and individual applications in specific areas (e.g., model calibration, water distribution systems, groundwater management, river-basin planning and management, etc.). However, there has been limited synthesis between shared problem traits, common EA challenges, and needed advances across major applications. This paper clarifies the current status and future research directions for better solving key water resources problems using EAs. Advances in understanding fitness landscape properties and their effects on algorithm performance are critical. Future EA-based applications to real-world problems require a fundamental shift of focus towards improving problem formulations, understanding general theoretic frameworks for problem decompositions, major advances in EA computational efficiency, and most importantly aiding real decision-making in complex, uncertain application contexts.

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“…Pohlheim [15] proposed in 1999 a visualisation system adapted to the Genetic and Evolutionary Algorithm Toolbox for Matlab -GEATbx [14]. His system allows various visualisation modes, and gives for instance the current state of a population (one generation), visualises a run (all the generations), or different runs for comparisons.…”

Section: On-line Visualisationmentioning

confidence: 99%

Visual Analysis of Population Scatterplots

Lutton

Foucquier

Perrot

et al. 2012

Lecture Notes in Computer Science

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Abstract. We investigate how visual analytic tools can deal with the huge amount of data produced during the run of an evolutionary algorithm. We show, on toy examples and on two real life problems, how a multidimensional data visualisation tool like ScatterDice/GraphDice can be easily used for analysing raw output data produced along the run of an evolutionary algorithm. Visual interpretation of population data is not used very often by the EA community for experimental analysis. We show here that this approach may yield additional high level information that is hardly accessible through conventional computation.

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Genetic and Evolutionary Algorithm Toolbox for Matlab

Cited by 98 publications

References 0 publications

Structural and Functional Sequence Test of Dynamic and State-Based Software with Evolutionary Algorithms

Structural and Functional Sequence Test of Dynamic and State-Based Software with Evolutionary Algorithms

Evolutionary algorithms and other metaheuristics in water resources: Current status, research challenges and future directions

Visual Analysis of Population Scatterplots

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