arXiv subjects

Fernando G. Lobo

Publications and source records attributed to Fernando G. Lobo.

16 recordsLinked to original sources

arXiv2022

When Hillclimbers Beat Genetic Algorithms in Multimodal Optimization

This paper investigates the performance of multistart next ascent hillclimbing and well-known evolutionary algorithms incorporating diversity preservation techniques on instances of the multimodal problem generator. This generator induces a class of problems in the bitstringdomain which is interesting to study from a theoretical perspective in the context of multimodal optimization, as it is a generalization of the classical OneMax and TwoMax functions for an arbitrary number of peaks. An average-case runtime analysis for multistart next ascent hill-climbing is presented for uniformly distributed equal-height instances of this class of problems. It is shown empirically that conventional niching and mating restriction techniques incorporated in an evolutionary algorithm are not sufficient to make them competitive with the hillclimbing strategy. We conjecture the reason for this behaviour is the lack of structure in the space of local optima on instances of this problem class, which makes an optimization algorithm unable to exploit information from one optimum to infer where another optimum might be. When no such structure exist, it seems that the best strategy for discovering all optima is a brute-force one. Overall, our study gives insights with respect to the adequacy of hillclimbers and evolutionary algorithms for multimodal optimization, depending on properties of the fitness landscape.

cs.NE↗

arXiv2015

A Java Implementation of the SGA, UMDA, ECGA, and HBOA

The Simple Genetic Algorithm, the Univariate Marginal Distribution Algorithm, the Extended Compact Genetic Algorithm, and the Hierarchical Bayesian Optimization Algorithm are all well known Evolutionary Algorithms. In this report we present a Java implementation of these four algorithms with detailed instructions on how to use each of them to solve a given set of optimization problems. Additionally, it is explained how to implement and integrate new problems within the provided set. The source and binary files of the Java implementations are available for free download at https://github.com/JoseCPereira/2015EvolutionaryAlgorithmsJava.

cs.NE↗

arXiv2015

A Java Implementation of Parameter-less Evolutionary Algorithms

The Parameter-less Genetic Algorithm was first presented by Harik and Lobo in 1999 as an alternative to the usual trial-and-error method of finding, for each given problem, an acceptable set-up of the parameter values of the genetic algorithm. Since then, the same strategy has been successfully applied to create parameter-less versions of other population-based search algorithms such as the Extended Compact Genetic Algorithm and the Hierarchical Bayesian Optimization Algorithm. This report describes a Java implementation, Parameter-less Evolutionary Algorithm (P-EAJava), that integrates several parameter-less evolutionary algorithms into a single platform. Along with a brief description of P-EAJava, we also provide detailed instructions on how to use it, how to implement new problems, and how to generate new parameter-less versions of evolutionary algorithms. At present time, P-EAJava already includes parameter-less versions of the Simple Genetic Algorithm, the Extended Compact Genetic Algorithm, the Univariate Marginal Distribution Algorithm, and the Hierarchical Bayesian Optimization Algorithm. The source and binary files of the Java implementation of P-EAJava are available for free download at https://github.com/JoseCPereira/2015ParameterlessEvolutionaryAlgorithmsJava.

cs.MS↗

arXiv2015

Java Implementation of a Parameter-less Evolutionary Portfolio

The Java implementation of a portfolio of parameter-less evolutionary algorithms is presented. The Parameter-less Evolutionary Portfolio implements a heuristic that performs adaptive selection of parameter-less evolutionary algorithms in accordance with performance criteria that are measured during running time. At present time, the portfolio includes three parameter-less evolutionary algorithms: Parameter-less Univariate Marginal Distribution Algorithm, Parameter-less Extended Compact Genetic Algorithm, and Parameter-less Hierarchical Bayesian Optimization Algorithm. Initial experiments showed that the parameter-less portfolio can solve various classes of problems without the need for any prior parameter setting technique and with an increase in computational effort that can be considered acceptable.

cs.MS↗

arXiv2013

An Optimized Divide-and-Conquer Algorithm for the Closest-Pair Problem in the Planar Case

We present an engineered version of the divide-and-conquer algorithm for finding the closest pair of points, within a given set of points in the XY-plane. For this version of the algorithm we show that only two pairwise comparisons are required in the combine step, for each point that lies in the 2 delta-wide vertical slab. The correctness of the algorithm is shown for all Minkowski distances with p>=1. We also show empirically that, although the time complexity of the algorithm is still O(n lg n), the reduction in the total number of comparisons leads to a significant reduction in the total execution time, for inputs with size sufficiently large.

cs.CG↗

arXiv2012

Affine Image Registration Transformation Estimation Using a Real Coded Genetic Algorithm with SBX

This paper describes the application of a real coded genetic algorithm (GA) to align two or more 2-D images by means of image registration. The proposed search strategy is a transformation parameters-based approach involving the affine transform. The real coded GA uses Simulated Binary Crossover (SBX), a parent-centric recombination operator that has shown to deliver a good performance in many optimization problems in the continuous domain. In addition, we propose a new technique for matching points between a warped and static images by using a randomized ordering when visiting the points during the matching procedure. This new technique makes the evaluation of the objective function somewhat noisy, but GAs and other population-based search algorithms have been shown to cope well with noisy fitness evaluations. The results obtained are competitive to those obtained by state-of-the-art classical methods in image registration, confirming the usefulness of the proposed noisy objective function and the suitability of SBX as a recombination operator for this type of problem.

cs.NE↗

arXiv2011

Idealized Dynamic Population Sizing for Uniformly Scaled Problems

This paper explores an idealized dynamic population sizing strategy for solving additive decomposable problems of uniform scale. The method is designed on top of the foundations of existing population sizing theory for this class of problems, and is carefully compared with an optimal fixed population sized genetic algorithm. The resulting strategy should be close to a lower bound in terms of what can be achieved, performance-wise, by self-adjusting population sizing algorithms for this class of problems.

cs.NE↗

arXiv2011

Evaluating Accessible Synchronous CMC Applications

This paper proposes a more comprehensive evaluation methodology to measure the usability and user experience qualities of accessible synchronous computer-mediated communication applications. The methodology goes beyond current practices by evaluating how the interaction between a user and a product influences the user experience of those at the other endpoint of the communication channel. A major contribution is given with the proposal of a user test where one of the participants tries to guess whether the other participant has a disability or not. The proposed test is inspired in the Turing Test, and is a consequence of user requirements elicited from a group of individuals with motor and speech disabilities. These ideas are tested and validated with two examples of synchronous communication applications.

cs.HC↗

arXiv2007

EasyVoice: Integrating voice synthesis with Skype

This paper presents EasyVoice, a system that integrates voice synthesis with Skype. EasyVoice allows a person with voice disabilities to talk with another person located anywhere in the world, removing an important obstacle that affect these people during a phone or VoIP-based conversation.

cs.CY↗

arXiv2007

A Virtual Logo Keyboard for People with Motor Disabilities

In our society, people with motor impairments are oftentimes socially excluded from their environment. This is unfortunate because every human being should have the possibility to obtain the necessary conditions to live a normal life. Although there is technology to assist people with motor impairments, few systems are targeted for programming environments. We have created a system, called Logo Keyboard, to assist people with motor disabilities to program with the Logo programming language. With this special keyboard we can help more people to get involved into computer programming and to develop projects in different areas.

cs.HC↗

arXiv2006

Breaking barriers for people with voice disabilities: Combining virtual keyboards with speech synthesizers, and VoIP applications

Text-to-speech technology has been broadly used to help people with voice disabilities to overcome their difficulties. With text-to-speech, a person types at a keyboard, the text is synthesized, and the sound comes out through the computer speakers. In recent years, Voice over IP (VoIP) applications have become very popular and have been used by people worldwide. These applications allow people to talk for free over the Internet and also to make traditional calls through the Public-Switched Telephone Network (PSTN) at a small fraction of the cost offered by traditional phone companies. We have created a system, called EasyVoice, which integrates speech synthesizers with VoIP applications. The result allows a person with motor impairments and voice disabilities to talk with another person located anywhere in the world. The benefits in this case are much stronger than the ones obtained by non-disabled people using VoIP applications. People with motor impairments sometimes can hardly use a regular or mobile phone. Thus, the advantage is not only the reduction in cost, but more important, the ability to talk at all.

cs.CY↗

arXiv2006

Revisiting Evolutionary Algorithms with On-the-Fly Population Size Adjustment

In an evolutionary algorithm, the population has a very important role as its size has direct implications regarding solution quality, speed, and reliability. Theoretical studies have been done in the past to investigate the role of population sizing in evolutionary algorithms. In addition to those studies, several self-adjusting population sizing mechanisms have been proposed in the literature. This paper revisits the latter topic and pays special attention to the genetic algorithm with adaptive population size (APGA), for which several researchers have claimed to be very effective at autonomously (re)sizing the population. As opposed to those previous claims, this paper suggests a complete opposite view. Specifically, it shows that APGA is not capable of adapting the population size at all. This claim is supported on theoretical grounds and confirmed by computer simulations.

cs.NE↗

arXiv2006

On the utility of the multimodal problem generator for assessing the performance of Evolutionary Algorithms

This paper looks in detail at how an evolutionary algorithm attempts to solve instances from the multimodal problem generator. The paper shows that in order to consistently reach the global optimum, an evolutionary algorithm requires a population size that should grow at least linearly with the number of peaks. It is also shown a close relationship between the supply and decision making issues that have been identified previously in the context of population sizing models for additively decomposable problems. The most important result of the paper, however, is that solving an instance of the multimodal problem generator is like solving a peak-in-a-haystack, and it is argued that evolutionary algorithms are not the best algorithms for such a task. Finally, and as opposed to what several researchers have been doing, it is our strong belief that the multimodal problem generator is not adequate for assessing the performance of evolutionary algorithms.

cs.NE↗

arXiv2004

An architecture for massive parallelization of the compact genetic algorithm

This paper presents an architecture which is suitable for a massive parallelization of the compact genetic algorithm. The resulting scheme has three major advantages. First, it has low synchronization costs. Second, it is fault tolerant, and third, it is scalable. The paper argues that the benefits that can be obtained with the proposed approach is potentially higher than those obtained with traditional parallel genetic algorithms. In addition, the ideas suggested in the paper may also be relevant towards parallelizing more complex probabilistic model building genetic algorithms.

cs.NE↗

arXiv2004

A philosophical essay on life and its connections with genetic algorithms

This paper makes a number of connections between life and various facets of genetic and evolutionary algorithms research. Specifically, it addresses the topics of adaptation, multiobjective optimization, decision making, deception, and search operators, among others. It argues that human life, from birth to death, is an adaptive or dynamic optimization problem where people are continuously searching for happiness. More important, the paper speculates that genetic algorithms can be used as a source of inspiration for helping people make decisions in their everyday life.

cs.NE↗

arXiv2004

Parameter-less Optimization with the Extended Compact Genetic Algorithm and Iterated Local Search

This paper presents a parameter-less optimization framework that uses the extended compact genetic algorithm (ECGA) and iterated local search (ILS), but is not restricted to these algorithms. The presented optimization algorithm (ILS+ECGA) comes as an extension of the parameter-less genetic algorithm (GA), where the parameters of a selecto-recombinative GA are eliminated. The approach that we propose is tested on several well known problems. In the absence of domain knowledge, it is shown that ILS+ECGA is a robust and easy-to-use optimization method.

cs.NE↗