45,456 research outputs found
Anytime Behavior of Inexact TSP Solvers and Perspectives for Automated Algorithm Selection
The Traveling-Salesperson-Problem (TSP) is arguably one of the best-known
NP-hard combinatorial optimization problems. The two sophisticated heuristic
solvers LKH and EAX and respective (restart) variants manage to calculate
close-to optimal or even optimal solutions, also for large instances with
several thousand nodes in reasonable time. In this work we extend existing
benchmarking studies by addressing anytime behaviour of inexact TSP solvers
based on empirical runtime distributions leading to an increased understanding
of solver behaviour and the respective relation to problem hardness. It turns
out that performance ranking of solvers is highly dependent on the focused
approximation quality. Insights on intersection points of performances offer
huge potential for the construction of hybridized solvers depending on instance
features. Moreover, instance features tailored to anytime performance and
corresponding performance indicators will highly improve automated algorithm
selection models by including comprehensive information on solver quality.Comment: This version has been accepted for publication at the IEEE Congress
on Evolutionary Computation (IEEE CEC) 2020, which is part of the IEEE World
Congress on Computational Intelligence (IEEE WCCI) 202
Experimental Analysis of Design Elements of Scalarizing Functions-based Multiobjective Evolutionary Algorithms
In this paper we systematically study the importance, i.e., the influence on
performance, of the main design elements that differentiate scalarizing
functions-based multiobjective evolutionary algorithms (MOEAs). This class of
MOEAs includes Multiobjecitve Genetic Local Search (MOGLS) and Multiobjective
Evolutionary Algorithm Based on Decomposition (MOEA/D) and proved to be very
successful in multiple computational experiments and practical applications.
The two algorithms share the same common structure and differ only in two main
aspects. Using three different multiobjective combinatorial optimization
problems, i.e., the multiobjective symmetric traveling salesperson problem, the
traveling salesperson problem with profits, and the multiobjective set covering
problem, we show that the main differentiating design element is the mechanism
for parent selection, while the selection of weight vectors, either random or
uniformly distributed, is practically negligible if the number of uniform
weight vectors is sufficiently large
Automated Algorithm Selection on Continuous Black-Box Problems By Combining Exploratory Landscape Analysis and Machine Learning
In this paper, we build upon previous work on designing informative and
efficient Exploratory Landscape Analysis features for characterizing problems'
landscapes and show their effectiveness in automatically constructing algorithm
selection models in continuous black-box optimization problems. Focussing on
algorithm performance results of the COCO platform of several years, we
construct a representative set of high-performing complementary solvers and
present an algorithm selection model that - compared to the portfolio's single
best solver - on average requires less than half of the resources for solving a
given problem. Therefore, there is a huge gain in efficiency compared to
classical ensemble methods combined with an increased insight into problem
characteristics and algorithm properties by using informative features. Acting
on the assumption that the function set of the Black-Box Optimization Benchmark
is representative enough for practical applications the model allows for
selecting the best suited optimization algorithm within the considered set for
unseen problems prior to the optimization itself based on a small sample of
function evaluations. Note that such a sample can even be reused for the
initial population of an evolutionary (optimization) algorithm so that even the
feature costs become negligible.Comment: This is the author's final version, and the article has been accepted
for publication in Evolutionary Computatio
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
Algorithms Inspired by Nature: A Survey
Nature is known to be the best optimizer. Natural processes most often than
not reach an optimal equilibrium. Scientists have always strived to understand
and model such processes.Thus, many algorithms exist today that are inspired by
nature. Many of these algorithms and heuristics can be used to solve problems
for which no polynomial time algorithms exist,such as Job Shop Scheduling and
many other Combinatorial Optimization problems. We will discuss some of these
algorithms and heuristics and how they help us solve complex problems of
practical importance
On the performance of multi-objective estimation of distribution algorithms for combinatorial problems
Fitness landscape analysis investigates features with a high influence on the
performance of optimization algorithms, aiming to take advantage of the
addressed problem characteristics. In this work, a fitness landscape analysis
using problem features is performed for a Multi-objective Bayesian Optimization
Algorithm (mBOA) on instances of MNK-landscape problem for 2, 3, 5 and 8
objectives. We also compare the results of mBOA with those provided by NSGA-III
through the analysis of their estimated runtime necessary to identify an
approximation of the Pareto front. Moreover, in order to scrutinize the
probabilistic graphic model obtained by mBOA, the Pareto front is examined
according to a probabilistic view. The fitness landscape study shows that mBOA
is moderately or loosely influenced by some problem features, according to a
simple and a multiple linear regression model, which is being proposed to
predict the algorithms performance in terms of the estimated runtime. Besides,
we conclude that the analysis of the probabilistic graphic model produced at
the end of evolution can be useful to understand the convergence and diversity
performances of the proposed approach.Comment: Accepted in IEEE WCCI/CEC '201
Evolutionary Optimization in an Algorithmic Setting
Evolutionary processes proved very useful for solving optimization problems.
In this work, we build a formalization of the notion of cooperation and
competition of multiple systems working toward a common optimization goal of
the population using evolutionary computation techniques. It is justified that
evolutionary algorithms are more expressive than conventional recursive
algorithms. Three subclasses of evolutionary algorithms are proposed here:
bounded finite, unbounded finite and infinite types. Some results on
completeness, optimality and search decidability for the above classes are
presented. A natural extension of Evolutionary Turing Machine model developed
in this paper allows one to mathematically represent and study properties of
cooperation and competition in a population of optimized species
An Experimental Study of Adaptive Control for Evolutionary Algorithms
The balance of exploration versus exploitation (EvE) is a key issue on
evolutionary computation. In this paper we will investigate how an adaptive
controller aimed to perform Operator Selection can be used to dynamically
manage the EvE balance required by the search, showing that the search
strategies determined by this control paradigm lead to an improvement of
solution quality found by the evolutionary algorithm
A Tribe Competition-Based Genetic Algorithm for Feature Selection in Pattern Classification
Feature selection has always been a critical step in pattern recognition, in
which evolutionary algorithms, such as the genetic algorithm (GA), are most
commonly used. However, the individual encoding scheme used in various GAs
would either pose a bias on the solution or require a pre-specified number of
features, and hence may lead to less accurate results. In this paper, a tribe
competition-based genetic algorithm (TCbGA) is proposed for feature selection
in pattern classification. The population of individuals is divided into
multiple tribes, and the initialization and evolutionary operations are
modified to ensure that the number of selected features in each tribe follows a
Gaussian distribution. Thus each tribe focuses on exploring a specific part of
the solution space. Meanwhile, tribe competition is introduced to the evolution
process, which allows the winning tribes, which produce better individuals, to
enlarge their sizes, i.e. having more individuals to search their parts of the
solution space. This algorithm, therefore, avoids the bias on solutions and
requirement of a pre-specified number of features. We have evaluated our
algorithm against several state-of-the-art feature selection approaches on 20
benchmark datasets. Our results suggest that the proposed TCbGA algorithm can
identify the optimal feature subset more effectively and produce more accurate
pattern classification
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
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