Journal ArticleDOI
Principles in the Evolutionary Design of Digital Circuits—Part II
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TLDR
It is argued that by studying evolved designs of gradually increasing scale, one might be able to discern new, efficient, and generalisable principles of design, which explain how to build systems which are too large to evolve.Abstract:
In a previous work it was argued that by studying evolved designs of gradually increasing scale, one might be able to discern new, efficient, and generalisable principles of design. These ideas are tested in the context of designing digital circuits, particularly arithmetic circuits. This process of discovery is seen as a principle extraction loop in which the evolved data is analysed both phenotypically and genotypically by processes of data mining and landscape analysis. The information extracted is then fed back into the evolutionary algorithm to enhance its search capabilities and hence increase the likelihood of identifying new principles which explain how to build systems which are too large to evolve.read more
Citations
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Journal ArticleDOI
Competitive coevolution through evolutionary complexification
TL;DR: It is argued that complexification, i.e. the incremental elaboration of solutions through adding new structure, achieves both these goals and is demonstrated through the NeuroEvolution of Augmenting Topologies (NEAT) method, which evolves increasingly complex neural network architectures.
Cartesian Genetic Programming.
TL;DR: The genotype–phenotype mapping used in CGP is one of its defining characteristics and its types are decided by the user and are listed in a function look-up table.
Proceedings Article
Efficient Reinforcement Learning Through Evolving Neural Network Topologies
TL;DR: NEAT shows that when structure is evolved with a principled method of crossover, by protecting structural innovation, and through incremental growth from minimal structure, learning is significantly faster and stronger than with the best fixed-topology methods.
Journal ArticleDOI
Redundancy and computational efficiency in Cartesian genetic programming
TL;DR: The results presented demonstrate the role of mutation and genotype length in the evolvability of the graph-based Cartesian genetic programming system and find that the most evolvable representations occur when the genotype is extremely large and in which over 95% of the genes are inactive.
Dissertation
Efficient evolution of neural networks through complexification
TL;DR: This dissertation presents the NeuroEvolution of Augmenting Topologies (NEAT) method, which makes search for complex solutions feasible and is first shown faster than traditional approaches on a challenging reinforcement learning benchmark task, and used to successfully discover complex behavior in three challenging domains.
References
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Book
Genetic algorithms in search, optimization, and machine learning
TL;DR: In this article, the authors present the computer techniques, mathematical tools, and research results that will enable both students and practitioners to apply genetic algorithms to problems in many fields, including computer programming and mathematics.
Genetic algorithms in search, optimization and machine learning
TL;DR: This book brings together the computer techniques, mathematical tools, and research results that will enable both students and practitioners to apply genetic algorithms to problems in many fields.
Book
Adaptation in natural and artificial systems
TL;DR: Names of founding work in the area of Adaptation and modiication, which aims to mimic biological optimization, and some (Non-GA) branches of AI.
Book
Time series analysis, forecasting and control
TL;DR: In this article, a complete revision of a classic, seminal, and authoritative book that has been the model for most books on the topic written since 1970 is presented, focusing on practical techniques throughout, rather than a rigorous mathematical treatment of the subject.
Book
Genetic Programming: On the Programming of Computers by Means of Natural Selection
TL;DR: This book discusses the evolution of architecture, primitive functions, terminals, sufficiency, and closure, and the role of representation and the lens effect in genetic programming.