68,931 research outputs found
Which Surrogate Works for Empirical Performance Modelling? A Case Study with Differential Evolution
It is not uncommon that meta-heuristic algorithms contain some intrinsic
parameters, the optimal configuration of which is crucial for achieving their
peak performance. However, evaluating the effectiveness of a configuration is
expensive, as it involves many costly runs of the target algorithm. Perhaps
surprisingly, it is possible to build a cheap-to-evaluate surrogate that models
the algorithm's empirical performance as a function of its parameters. Such
surrogates constitute an important building block for understanding algorithm
performance, algorithm portfolio/selection, and the automatic algorithm
configuration. In principle, many off-the-shelf machine learning techniques can
be used to build surrogates. In this paper, we take the differential evolution
(DE) as the baseline algorithm for proof-of-concept study. Regression models
are trained to model the DE's empirical performance given a parameter
configuration. In particular, we evaluate and compare four popular regression
algorithms both in terms of how well they predict the empirical performance
with respect to a particular parameter configuration, and also how well they
approximate the parameter versus the empirical performance landscapes
Algorithm Portfolio for Individual-based Surrogate-Assisted Evolutionary Algorithms
Surrogate-assisted evolutionary algorithms (SAEAs) are powerful optimisation
tools for computationally expensive problems (CEPs). However, a randomly
selected algorithm may fail in solving unknown problems due to no free lunch
theorems, and it will cause more computational resource if we re-run the
algorithm or try other algorithms to get a much solution, which is more serious
in CEPs. In this paper, we consider an algorithm portfolio for SAEAs to reduce
the risk of choosing an inappropriate algorithm for CEPs. We propose two
portfolio frameworks for very expensive problems in which the maximal number of
fitness evaluations is only 5 times of the problem's dimension. One framework
named Par-IBSAEA runs all algorithm candidates in parallel and a more
sophisticated framework named UCB-IBSAEA employs the Upper Confidence Bound
(UCB) policy from reinforcement learning to help select the most appropriate
algorithm at each iteration. An effective reward definition is proposed for the
UCB policy. We consider three state-of-the-art individual-based SAEAs on
different problems and compare them to the portfolios built from their
instances on several benchmark problems given limited computation budgets. Our
experimental studies demonstrate that our proposed portfolio frameworks
significantly outperform any single algorithm on the set of benchmark problems
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Evolving structure-function mappings in cognitive neuroscience using genetic programming
A challenging goal of psychology and neuroscience is to map cognitive functions onto neuroanatomical structures. This paper shows how computational methods based upon evolutionary algorithms can facilitate the search for satisfactory mappings by efficiently combining constraints from neuroanatomy and physiology (the structures) with constraints from behavioural experiments (the functions). This methodology involves creation of a database coding for known neuroanatomical and physiological constraints, for mental programs made of primitive cognitive functions, and for typical experiments with their behavioural results. The evolutionary algorithms evolve theories mapping structures to functions in order to optimize the fit with the actual data. These theories lead to new, empirically testable predictions. The role of the prefrontal cortex in humans is discussed as an example. This methodology can be applied to the study of structures or functions alone, and can also be used to study other complex systems.
(This article does not exactly replicate the final version published in the Journal of Swiss Psychology. It is not a copy of the original published article and is not suitable for citation.
A Novel Genetic Algorithm using Helper Objectives for the 0-1 Knapsack Problem
The 0-1 knapsack problem is a well-known combinatorial optimisation problem.
Approximation algorithms have been designed for solving it and they return
provably good solutions within polynomial time. On the other hand, genetic
algorithms are well suited for solving the knapsack problem and they find
reasonably good solutions quickly. A naturally arising question is whether
genetic algorithms are able to find solutions as good as approximation
algorithms do. This paper presents a novel multi-objective optimisation genetic
algorithm for solving the 0-1 knapsack problem. Experiment results show that
the new algorithm outperforms its rivals, the greedy algorithm, mixed strategy
genetic algorithm, and greedy algorithm + mixed strategy genetic algorithm
Towards a Theory-Guided Benchmarking Suite for Discrete Black-Box Optimization Heuristics: Profiling EA Variants on OneMax and LeadingOnes
Theoretical and empirical research on evolutionary computation methods
complement each other by providing two fundamentally different approaches
towards a better understanding of black-box optimization heuristics. In
discrete optimization, both streams developed rather independently of each
other, but we observe today an increasing interest in reconciling these two
sub-branches. In continuous optimization, the COCO (COmparing Continuous
Optimisers) benchmarking suite has established itself as an important platform
that theoreticians and practitioners use to exchange research ideas and
questions. No widely accepted equivalent exists in the research domain of
discrete black-box optimization.
Marking an important step towards filling this gap, we adjust the COCO
software to pseudo-Boolean optimization problems, and obtain from this a
benchmarking environment that allows a fine-grained empirical analysis of
discrete black-box heuristics. In this documentation we demonstrate how this
test bed can be used to profile the performance of evolutionary algorithms.
More concretely, we study the optimization behavior of several EA
variants on the two benchmark problems OneMax and LeadingOnes. This comparison
motivates a refined analysis for the optimization time of the EA
on LeadingOnes
PasMoQAP: A Parallel Asynchronous Memetic Algorithm for solving the Multi-Objective Quadratic Assignment Problem
Multi-Objective Optimization Problems (MOPs) have attracted growing attention
during the last decades. Multi-Objective Evolutionary Algorithms (MOEAs) have
been extensively used to address MOPs because are able to approximate a set of
non-dominated high-quality solutions. The Multi-Objective Quadratic Assignment
Problem (mQAP) is a MOP. The mQAP is a generalization of the classical QAP
which has been extensively studied, and used in several real-life applications.
The mQAP is defined as having as input several flows between the facilities
which generate multiple cost functions that must be optimized simultaneously.
In this study, we propose PasMoQAP, a parallel asynchronous memetic algorithm
to solve the Multi-Objective Quadratic Assignment Problem. PasMoQAP is based on
an island model that structures the population by creating sub-populations. The
memetic algorithm on each island individually evolve a reduced population of
solutions, and they asynchronously cooperate by sending selected solutions to
the neighboring islands. The experimental results show that our approach
significatively outperforms all the island-based variants of the
multi-objective evolutionary algorithm NSGA-II. We show that PasMoQAP is a
suitable alternative to solve the Multi-Objective Quadratic Assignment Problem.Comment: 8 pages, 3 figures, 2 tables. Accepted at Conference on Evolutionary
Computation 2017 (CEC 2017
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