This paper studies the characteristics of a new composite evolutionary computation algorithm in which genetic evolution, individual learning and social learning interact in NK fitness landscape. We derive conditions for effective social learning in static and dynamic environments using computer simulations of a model of the composite evolutionary algorithm. The conditions for static environment...

This paper uses the recent idea that the fundamental haploid-diploid lifecycle of eukaryotic organisms implements a rudimentary form of learning within evolution. A general approach for evolutionary computation is here derived that differs from all previous known work using diploid representations. The primary role of recombination is also changed from that previously considered in both natural...

Up to now there is no standardized strategy in how to teach evolutionary algorithms. Surely the answer to this question depends on the teaching context and the target group. But in the field of scientific teaching it is essential to abstract from paradigm orientation to a more general concept of evolutionary computing and to the underlying foundation. At the University of Stuttgart we pursue th...

We present a new method for proving lower bounds in evolutionary computation based on fitness-level arguments and an additional condition on transition probabilities between fitness levels. The method yields exact or near-exact lower bounds for LO, OneMax, and all functions with a unique optimum. All lower bounds hold for every evolutionary algorithm that only uses standard mutation as variatio...

As early as the very beginnings of the science of computation, in the times of Charles Babbage and his “analytical engine”, search has been recognized as an important conceptual tool for problem solving. Later, in the initial stages of modern computational theory and artificial intelligence, Alan Turing proposed various kinds of search as a means to achieve machine intelligence. Since then, sea...

A general introduction to artificial intelligence methods of measurement signal processing is given in Article 128, Nature and Scope of AI Techniques, Volume 2. In nature, evolution is mostly determined by natural selection or different individuals competing for resources in the environment. Those individuals that are better are more likely to survive and propagate their genetic material. The e...

A key issue in software evolution analysis is being able to compute evolutionary change accurately and with rich semantics. This dissertation describes a mathematical framework for enabling accurate computation of semantic evolutionary change. It is based on graphs for representing software semantics, graph transformations for modeling evolution, and effects of graph transformations for capturi...

Although evolutionary computation (EC) is an “embarrassingly parallel” process, it is often deployed on essentially serial machines, and even its parallel implementations typically retain the globally synchronized and regimented style typical of serial computation. We explore a radical ‘physical evolutionary computation’ (PEC) hardware/software framework that is based on real time and real spac...