Gradient subspace approximation: a direct search method for memetic computing

The hybridization of evolutionary algorithms and local search techniques as, e.g., mathematical programming techniques, also referred to as memetic algorithms, has caught the interest of many researchers in the recent past. Reasons for this include that the resulting algorithms are typically robust...

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Bibliographic Details
Published in:Soft computing (Berlin, Germany) Vol. 21; no. 21; pp. 6331 - 6350
Main Authors: Schütze, Oliver, Alvarado, Sergio, Segura, Carlos, Landa, Ricardo
Format: Journal Article
Language:English
Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.11.2017
Springer Nature B.V
Subjects:
Algorithms
Approximation
Artificial Intelligence
Computational Intelligence
Control
Engineering
Evolutionary algorithms
Evolutionary computation
Heuristic
Mathematical Logic and Foundations
Mathematical programming
Mechatronics
Methodologies and Application
Neighborhoods
Optimization
Robotics
Robustness (mathematics)
Search methods
Memetic computing
Differential evolution
Direct search
Evolutionary algorithms
Scalar optimization
ISSN:1432-7643, 1433-7479
Online Access:Get full text
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Summary:The hybridization of evolutionary algorithms and local search techniques as, e.g., mathematical programming techniques, also referred to as memetic algorithms, has caught the interest of many researchers in the recent past. Reasons for this include that the resulting algorithms are typically robust and reliable since they take the best of both worlds. However, one crucial drawback of such hybrids is the relatively high cost of the local search techniques since many of them require the gradient or even the Hessian at each candidate solution. Here, we propose an alternative way to compute search directions by exploiting the neighborhood information. That is, for a given point within a population P , the neighboring solutions in P are used to compute the most greedy search direction out of the given data. The method is hence particularly interesting for the usage within population-based search strategies since the search directions come ideally for free in terms of additional function evaluations. In this study, we analyze the novel method first as a stand-alone algorithm and show further on its benefit as a local searcher within differential evolution.
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ISSN:1432-7643
1433-7479
DOI:10.1007/s00500-016-2187-x