Genetic set recombination and its application to neural network topology optimisation

Radcliffe, Nicholas J.

doi:10.1007/bf01411376

Cited by 90 publications

(55 citation statements)

References 6 publications

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“…The numbers are inherited during crossover unchanged, and allow NEAT to perform crossover without the need for expensive topological analysis. That way, genomes of different organizations and sizes stay compatible throughout evolution, solving the previously open problem of matching different topologies [66] in an evolving population.…”

Section: Cppn-neatmentioning

confidence: 99%

Compositional pattern producing networks: A novel abstraction of development

Stanley

2007

Genet Program Evolvable Mach

605

461

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Natural DNA can encode complexity on an enormous scale. Researchers are attempting to achieve the same representational efficiency in computers by implementing developmental encodings, i.e. encodings that map the genotype to the phenotype through a process of growth from a small starting point to a mature form. A major challenge in in this effort is to find the right level of abstraction of biological development to capture its essential properties without introducing unnecessary inefficiencies. In this paper, a novel abstraction of natural development, called Compositional Pattern Producing Networks (CPPNs), is proposed. Unlike currently accepted abstractions such as iterative rewrite systems and cellular growth simulations, CPPNs map to the phenotype without local interaction, that is, each individual component of the phenotype is determined independently of every other component. Results produced with CPPNs through interactive evolution of twodimensional images show that such an encoding can nevertheless produce structural motifs often attributed to more conventional developmental abstractions, suggesting that local interaction may not be essential to the desirable properties of natural encoding in the way that is usually assumed.

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Section: Cppn-neatmentioning

confidence: 99%

Compositional pattern producing networks: A novel abstraction of development

Stanley

2007

Genet Program Evolvable Mach

605

461

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“…Principled crossover uses the lineage tracking to limit crossover to common genes across solutions when producing children to minimize the "competing conventions problem." 55 A solution produced by MFF-NEAT here is referred to as a "system" comprising a "gating network" and zero or more "expert networks." The number of expert networks and the individual network architectures are dictated by the algorithm given the problem.…”

Section: Mff-neatmentioning

confidence: 99%

The importance of physicochemical characteristics and nonlinear classifiers in determining HIV-1 protease specificity

Manning

Walsh

2016

Bioengineered

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This paper reviews recent research relating to the application of bioinformatics approaches to determining HIV-1 protease specificity, outlines outstanding issues, and presents a new approach to addressing these issues. Leading machine learning theory for the problem currently suggests that the direct encoding of the physicochemical properties of the amino acid substrates is not required for optimal performance. A number of amino acid encoding approaches which incorporate potentially relevant physicochemical properties of the substrate are identified, and are evaluated using a nonlinear task decomposition based neuroevolution algorithm. The results are evaluated, and compared against a recent benchmark set on a nonlinear classifier using only amino acid sequence and identity information. Ensembles of these nonlinear classifiers using the physicochemical properties of the substrate are demonstrated to consistently outperform the recently published state-of-the-art linear support vector machine based approach in out-of-sample evaluations.

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“…Historical markings allow NEAT to perform crossover without expensive topological analysis. Genomes of different organizations and sizes stay compatible throughout evolution, and the problem of matching different topologies (Radcliffe 1993) is essentially avoided.…”

Section: Algorithm 2 Neat(smentioning

confidence: 99%

Adaptive Representations for Reinforcement Learning

Stone

Whiteson

2010

Studies in Computational Intelligence

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AcknowledgmentsFirst and foremost, I would like to thank my parents, without whose tireless support and encouragement none of my achievements, academic or otherwise, would have been possible.In particular, I am grateful to my father, Adam, for instilling in me a deep enmity for ignorance, a lasting appreciation for the beauty of mathematics, and an uncompromising respect for the rigor of science. I am grateful to my mother, Rena, for convincing me that I was capable of achieving any goal I set and for instilling in me a wellspring of self-confidence which sustains me to this day.I am also thankful to my brothers for their guidance, advice, and mentorship as well as for the often contrasting examples they set for me. I am grateful to Yoseif for convincing me that computers are fun, for showing me how to use them, and for introducing me to the art of computer programming by teaching me BASIC when I was seven years old. I am grateful to Daniel for setting an example of stratospheric success, for treating me like a friend rather than a little brother, and for offering a bottomless well of empathy for all the trials of graduate school and life in general. In reinforcement learning, an autonomous agent seeks an effective control policy for tackling a sequential decision task. Unlike in supervised learning, the agent never sees examples of correct or incorrect behavior but receives only a reward signal as feedback. One limitation of current methods is that they typically require a human to manually design a representation for the solution (e.g. the internal structure of a neural network). Since poor design choices can lead to grossly suboptimal policies, agents that automatically adapt their own representations have the potential to dramatically improve performance. This thesis introduces two novel approaches for automatically discovering high-performing representations.The first approach synthesizes temporal difference methods, the traditional approach to reinforcement learning, with evolutionary methods, which can learn representations for vi a broad class of optimization problems. This synthesis is accomplished via 1) on-line evolutionary computation, which customizes evolutionary methods to the on-line nature of most reinforcement learning problems, and 2) evolutionary function approximation, which evolves representations for the value function approximators that are critical to the temporal difference approach.The second approach, called adaptive tile coding, automatically learns representations based on tile codings, which form piecewise-constant approximations of value functions. It begins with coarse representations and gradually refines them during learning, analyzing the current policy and value function to deduce the best refinements.This thesis also introduces a novel method for devising input representations. In particular, it presents a way to find a minimal set of features sufficient to describe the agent's current state, a challenge known as the feature selection problem. The technique, called Feature ...

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Genetic set recombination and its application to neural network topology optimisation

Abstract: Forma analysis is applied to the task of optimising

Cited by 90 publications

References 6 publications

Compositional pattern producing networks: A novel abstraction of development

Compositional pattern producing networks: A novel abstraction of development

The importance of physicochemical characteristics and nonlinear classifiers in determining HIV-1 protease specificity

Adaptive Representations for Reinforcement Learning

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