Juan-Julián Merelo-Guervós

Finding a needle in a haystack using hints and evolutionary computation: the case of evolutionary MasterMind

In this paper we present a new version of an evolutionary algorithm that finds the hidden combination in the game of MasterMind by using hints on how close is a combination played to it. The evolutionary algorithm finds the hidden combination in an optimal number of guesses, is efficient in terms of memory and CPU, and examines only a minimal part of the search space. The algorithm is fast, and indeed previous versions can be played in real time on the world wide web. This new version of the algorithm is presented and compared with theoretical bounds and other algorithms. We also examine how the algorithm scales with search space size, and its performance for different values of the EA parameters. # 2005 Published by Elsevier B.V.

Service oriented evolutionary algorithms

This work presents a service oriented architecture for evolutionary algorithms, and an implementation of this architecture using a specific technology (called OSGiLiath). Service oriented architecture is a computational paradigm where users interact using services to increase the integration between systems. The presented abstract architecture is formed by loosely coupled, highly configurable and language-independent services. As an example of an implementation of this architecture, a complete process development using a specific service oriented technology is explained. With this implementation, less effort than classical development in integration, distribution mechanisms and execution time management has been attained. In addition, steps, ideas, advantages and disadvantages, and guidelines to create service oriented evolutionary algorithms are presented. Using existing software, or from scratch, researchers can create services to increase the interoperability in this area.

Pool-Based distributed evolutionary algorithms using an object database

This work presents the mapping of an evolutionary algorithm to the CouchDB object store. This mapping decouples the population from the evolutionary algorithm, and allows a distributed and asynchronous operation of clients written in different languages. In this paper we present initial tests which prove that the novel algorithm design still performs as an evolutionary algorithm and try to find out what are the main issues concerning it, what kind of speedups should we expect, and how all this affects the fundamentals of the evolutionary algorithm.

Dealing with noisy fitness in the design of a RTS game bot

This work describes an evolutionary algorithm (EA) for evolving the constants, weights and probabilities of a rule-based decision engine of a bot designed to play the Planet Wars game. The evaluation of the individuals is based on the result of some non-deterministic combats, whose outcome depends on random draws as well as the enemy action, and is thus noisy. This noisy fitness is addressed in the EA and then, its effects are deeply analysed in the experimental section. The conclusions shows that reducing randomness via repeated combats and re-evaluations reduces the effect of the noisy fitness, making then the EA an effective approach for solving the problem.

Validating a peer-to-peer evolutionary algorithm

This paper proposes a simple experiment for validating a Peer-to-Peer Evolutionary Algorithm in a real computing infrastructure in order to verify that results meet those obtained by simulations. The validation method consists of conducting a well-characterized experiment in a large computer cluster of up to a number of processors equal to the population estimated by the simulator. We argue that the validation stage is usually missing in the design of large-scale distributed meta-heuristics given the difficulty of harnessing a large number of computing resources. That way, most of the approaches in the literature focus on studying the model viability throughout a simulation-driven experimentation. However, simulations assume idealistic conditions that can influence the algorithmic performance and bias results when conducted in a real platform. Therefore, we aim at validating simulations by running a real version of the algorithm. Results show that the algorithmic performance is rather accurate to the predicted one whilst times-to-solutions can be drastically decreased when compared to the estimation of a sequential run.

Testing diversity-enhancing migration policies for hybrid on-line evolution of robot controllers

We investigate on-line on-board evolution of robot controllers based on the so-called hybrid approach (island-based). Inherently to this approach each robot hosts a population (island) of evolving controllers and exchanges controllers with other robots at certain times. We compare different exchange (migration) policies in order to optimize this evolutionary system and compare the best hybrid setup with the encapsulated and distributed alternatives. We conclude that adding a difference-based migrant selection scheme increases the performance.

Cloudy distributed evolutionary computation

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Alife in the Clouds: A Short Review of Applications of Artificial Life to Cloud Computing and Back

Cloud computing is currently the prevailing mode of designing, creating and deploying complex applications, and it has implied a paradigm shift in all three areas; even if it taps and extends previous concepts such as service oriented, concurrent and distributed computing, this shift has to be eventually translated to the algorithmic and conceptual aspects of sciences such as artificial life. In this short paper we will review of how the world of cloud computing has intersected the artificial life field, and how it has been used as an inspiration for new models or the design and implementation of new and powerful algorithms; in the other direction, we will also examine how artificial life concepts such as self-organization have been applied to the field of cloud computing.

Ranking Programming Languages for Evolutionary Algorithm Operations

In this paper we measure the speed of several popular and recent programming languages performing the most usual operators in the canonical evolutionary algorithm, mutation and crossover, as well as an usual fitness function, OneMax. These three operations are representative of the kind of the ones performed in binary chromosomes. Our main objectives are, first, to create programs in programming languages that use the fastest available implementation. Second, to find out the differences in speeds for the different languages. Third, to find out whether the usual assumptions about the speed of languages really holds. And, finally, to find if the assumed order of speed in languages used in evolutionary algorithms holds true for all kinds of operations. In order to do that, we use available implementations or perform our own, concluding that the evolutionary algorithm scenario is more complex than usually assumed and finding out some surprising winners and losers among the languages tested.

Performance improvements of evolutionary algorithms in perl 6

Perl 6 is a recently released language that belongs to the Perl family but was actually designed from scratch, not as a refactoring of the Perl 5 codebase. Through its two-year-old (released) history, it has increased performance by several orders of magnitude, arriving recently to the point where it can be safely used in production. In this paper, we are going to compare the historical and current performance of Perl 6 in a single problem, OneMax, to those of other interpreted languages; besides, we will also use implicit concurrency and see what kind of performance and scaling can we expect from it.