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More than a decade after the first extensive overview on parameter control, we revisit the field and present a survey of the state-of-the-art. We briefly summarize the development of the field and discuss existing work related to each major parameter or component of an evolutionary algorithm. Based on this overview, we observe trends in the area, identify some (methodological) shortcomings, and give...
We propose a new scenario for the evolution of robot morphologies based on an egg metaphor. A swarm of robots is released in a large arena in which they form organisms through a process of morphogenesis. These organisms can reproduce by fertilising eggs in their vicinity, these fertilised eggs in turn build new organisms by recruiting free modules as a ‘seed’. We investigate the influence of three...
On-line, on-board evolution of robot controllers implies an inherent need for adjusting the parameters of the evolutionary algorithm on-the-fly. In this paper we argue that the most influential factor to govern evolution in our application is the mutation operator. To address the problem of adjusting its parameter(s) we identify different on-line parameter control mechanisms and perform an experimental...
In this paper we investigate how life expectation influences the development of risk attitude within an artificial species. Our hypothesis is that agents with a very long life span are likely to become more risk averse because they have more to lose. To assess this hypothesis we set up a simple system, based on Sugarscape, where risk attitude is an inheritable (hence, evolvable) property. Performing...
In this paper we investigate and compare three aesthetic measures within the context of evolutionary art. We evolve visual art with an unsupervised evolutionary art system using genetic programming and an aesthetic measure as the fitness function. We perform multiple experiments with different aesthetic measures and examine their influence on the evolved images. Additionally, we perform a cross-evaluation...
This paper describes and experimentally evaluates the viability of the (μ + 1) ON-LINE evolutionary algorithm for on-line adaptation of robot controllers. Secondly, it explores the parameter space for this algorithm and identifies four important parameters: the population size μ, the re-evaluation rate ρ, the mutation step-size σ and the controller evaluation period τ. Subsequently, it investigates...
We present a case study demonstrating that using the REVAC parameter tuning method we can greatly improve the `world champion' EA (the winner of the CEC-2005 competition) with little effort. For `normal' EAs the margins for possible improvements are likely much bigger. Thus, the main message of this paper is that using REVAC great performance improvements are possible for many EAs at moderate costs...
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