Titlebook: Genetic Programming; 17th European Confer Miguel Nicolau,Krzysztof Krawiec,Kevin Sim Conference proceedings 2014 Springer-Verlag Berlin Hei

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https://doi.org/10.1007/978-94-011-0637-5. The input space . is composed of observational data of the form (.., .(..)), .?=?1... . where each .. denotes a k-dimensional input vector of design variables and . is the response. Genetic Programming (GP) is used to transform the original input space . into a new input space .?=?(.., .(..)) that

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https://doi.org/10.1007/978-1-4842-9253-2stems, and evolutionary robotics. To date most research on this task has been described in terms of developments to reinforcement learning with function approximation or frameworks for neuro-evolution. This work performs an initial study using a recently proposed algorithm for evolving teams of prog

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https://doi.org/10.1007/978-3-662-67556-4icroprogram. Linear genetic programming is extended to evolve a program for the controller together with suitable hardware architecture. Experimental results show that the platform can automatically design general solutions as well as highly optimized specialized solutions to benchmark problems such

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Dominik Lis,Joshua Gelhaar,Boris Ottodecisions allows the systems to utilise their manufacturing resources better and achieve higher total profit. Therefore, finding optimal solutions for OAS is desirable. Unfortunately, the exact optimisation approaches previously proposed for OAS are still very time consuming and usually fail to solv

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