Classifier systems and genetic algorithms

TL;DR: The proposed slime mould algorithm has several new features with a unique mathematical model that uses adaptive weights to simulate the process of producing positive and negative feedback of the propagation wave of slime mould based on bio-oscillator to form the optimal path for connecting food with excellent exploratory ability and exploitation propensity.

TL;DR: The purpose, the general structure, and the working principles of different approaches, including genetic algorithms (GA), evolution strategies (ES), and evolutionary programming (EP) are described by analysis and comparison of their most important constituents (i.e. representations, variation operators, reproduction, and selection mechanism).

TL;DR: This paper provides its own perspective view in which the goal is to build self-adaptive learners that improve their bias dynamically through experience by accumulating meta-knowledge, and provides a survey of meta-learning as reported by the machine-learning literature.

TL;DR: Different models of genetic operators and some mechanisms available for studying the behaviour of this type of genetic algorithms are revised and compared.

TL;DR: The aim of the book is to advance the understanding of how humans think by putting forth a theory of human problem solving, along with a body of empirical evidence that permits assessment of the theory.

TL;DR: In this article, a model of such a system is given, based on aspects of neurobiology but readily adapted to integrated circuits, and the collective properties of this model produce a content-addressable memory which correctly yields an entire memory from any subpart of sufficient size.

TL;DR: This paper regards learning as the phenomenon of knowledge acquisition in the absence of explicit programming, and gives a precise methodology for studying this phenomenon from a computational viewpoint.