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.

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.

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.

When Hillclimbers Beat Genetic Algorithms in Multimodal Optimization

It has been shown in the past that a multistart hillclimbing strategy compares favourably to a standard genetic algorithm with respect to solving instances of the multimodal problem generator. We extend that work and verify if the utilization of diversity preservation techniques in the genetic algorithm changes the outcome of the comparison. We do so under two scenarios: (1) when the goal is to find the global optimum, (2) when the goal is to find all optima. A mathematical analysis is performed for the multistart hillclimbing algorithm and a through empirical study is conducted for solving instances of the multimodal problem generator with increasing number of optima, both with the hillclimbing strategy as well as with genetic algorithms with niching. Although niching improves the performance of the genetic algorithm, it is still inferior to the multistart hillclimbing strategy on this class of problems. An idealized niching strategy is also presented and it is argued that its performance should be close to a lower bound of what any evolutionary algorithm can do on this class of problems.

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.

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.

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.

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.

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.

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.