This paper focuses on the phenomena of evolution whose appearance is notable because no explicit mutation, recombination or artificial selection operators are introduced. We call the system self-evolving because every variation is performed by the objects themselves in their machine form. Keywords: artificial chemistry, autocatalytic reaction system, molecular computing, prebiotic evolution, self-organization, self-programming 1

Abstract not found

This paper presents the evolving objects library (EOlib), an object-oriented framework for evolutionary computation (EC) that aims to provide a exible set of classes to build EC applications. EOlib design objective is to be able to evolve any object in which tness makes sense.

Abstract not found

An account of grammatical acquisition is developed within the parameter-setting framework applied to a generalized categorial grammar (GCG). The GCG is embedded in a default inheritance network yielding a natural partial ordering (reflecting generality) of parameters which determines a partial order for parameter setting. Computational simulation shows that several resulting acquisition procedures are effective on a grammar / language set expressing major typological distinctions based on constituent order, and defining 70 distinct full languages and over 200 subset languages. The effects on acquisition of maturational working memory limitations, trigger presentation sequences, parameter update criteria, and differing initial settings are explored via computational simulation. Computational simulations of populations of language learners / users instantiating the model show: 1) that variant acquisition procedures with differing constraints and biases exert differing selective pressures on the evolution of language; 2) acquisition procedures will evolve towards more efficient variants in the environment of adaptation. The reciprocal evolution of language acquisition procedures and of language creates a genuinely coevolutionary dynamic, despite the relative speed of linguistic selection for language variants compared to natural selection for variant language acquisition procedures.

As any other biological trait, communication can be studied under at least four perspectives: mechanistic, ontogenetic, functional, and phylogenetic (Tinbergen, 1963). Here we focus on the following phylogenetic question: how can communication emerge given that both signal-producing and signal-responding abilities seem to be adaptively neutral until the complementary ability is present in the population? We explore the problem of co-evolution of speakers and hearers with artificial life simulations: a population of artificial neural networks evolving a food call system. The core of the paper is devoted to the careful analysis of the complex evolutionary dynamics demonstrated by our simple simulation. Our analyzes reveal an important factor which might solve the phylogenetic problem: the spontaneous production of good (meaningful) signals by speakers due to the need for organisms to categorize their experience in adaptively relevant ways. We discuss our results with respect both to previous simulative work and to the biological literature on the evolution of communication.