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Condition: Refurbished. We also ship no later than next business day. Thank you for your business. Seller Inventory A Condition: As New. An apparently unread copy in perfect condition. Dust cover is intact; pages are clean and are not marred by notes or folds of any kind. Introduction to Evolutionary Computing Springer, Ashlock, D. Evolutionary Computation for Modeling and Optimization Springer, De Jong, K.
Wang, C. Highly efficient light-trapping structure design inspired by natural evolution. Schmidt, M.
The New Experimentalism
Distilling free-form natural laws from experimental data. Science , 81—85 This paper provides a forceful demonstration of the power of evolutionary methods for tasks that are thought to require highly educated scientists to perform. Embodied artificial evolution: artificial evolutionary systems in the 21st Century. Branke, J. Piperno, D. Starch grain and phytolith evidence for early ninth millennium B.
Natl Acad. USA , — Akey, J. Tracking footprints of artificial selection in the dog genome. Dennett, D. Darwin's Dangerous Idea Penguin, Goldberg, D.
- Infinite Linear Groups: An Account of the Group-theoretic Properties of Infinite Groups of Matrices.
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Fogel, D. Evolution and Optimum Seeking Wiley, Banzhaf, W. Genetic Programming: an Introduction Morgan Kaufmann, Storn, R.
Differential evolution — a simple and efficient heuristic for global optimization over continuous spaces. Price, K.
Experimental Research in Evolutionary Computation : The New Experimentalism
Kennedy, J. Particle swarm optimization. In Proc. Swarm Intelligence Morgan Kaufmann, Are genetic algorithms function optimizers? Hornby, G. Arias-Montano, A. Multiobjective evolutionary algorithms in aeronautical and aerospace engineering. IEEE Trans. Besnard, J. Automated design of ligands to polypharmacological profiles.
Nature , — Huyer, W. A comparison of global search algorithms for continuous black box optimization. Hansen, N. Completely derandomized self-adaptation in evolution strategies.
This article introduced the CMA-ES algorithm, widely regarded as the state of the art in numerical optimization. Contemporary Evolution Strategies Springer, Yao, X. Evolving artificial neural networks. IEEE 87 , — Fink Prize Paper Award, brought together different strands of research and drew attention to the potential benefits of combining these two forms of learning.
Floreano, D. Neuroevolution: from architectures to learning.follow url
Experimental Research in Evolutionary Computation: The New Experimentalism - PDF Free Download
Barros, R. A survey of evolutionary algorithms for decision-tree induction. Man Cybern. C 42 , — Widera, P. GP challenge: evolving energy function for protein structure prediction. Evolvable Mach. A combined machine learning and genetic algorithm approach to controller design. Watson, R. Embodied evolution: distributing an evolutionary algorithm in a population of robots. Bredeche, N. Environment-driven distributed evolutionary adaptation in a population of autonomous robotic agents. Nolfi, S. Bongard, J. Evolutionary robotics. ACM 56 , 74—85 Evolution of adaptive behavior in robots by means of Darwinian selection.
PLoS Biol. Hinton, G. How learning can guide evolution. Complex Syst. This seminal paper showed that learning can guide evolution even though characteristics acquired by the phenotype are not communicated to the genotype.
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- Experimental Research in Evolutionary Computation : The New Experimentalism;
Borenstein, E. The effect of phenotypic plasticity on evolution in multipeaked fitness landscapes. Paenke, I. Balancing population and individual level of adaptation in changing environments. Chen, X.
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Multi-facet survey on memetic computation. Krasnogor, N. It seems that you're in Germany. We have a dedicated site for Germany. Experimentation is necessary - a purely theoretical approach is not reasonable. The new experimentalism, a development in the modern philosophy of science, considers that an experiment can have a life of its own.
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It provides a statistical methodology to learn from experiments, where the experimenter should distinguish between statistical significance and scientific meaning. This book introduces the new experimentalism in evolutionary computation, providing tools to understand algorithms and programs and their interaction with optimization problems. The book develops and applies statistical techniques to analyze and compare modern search heuristics such as evolutionary algorithms and particle swarm optimization. Treating optimization runs as experiments, the author offers methods for solving complex real-world problems that involve optimization via simulation, and he describes successful applications in engineering and industrial control projects.
The book bridges the gap between theory and experiment by providing a self-contained experimental methodology and many examples, so it is suitable for practitioners and researchers and also for lecturers and students. It summarizes results from the author's consulting to industry and his experience teaching university courses and conducting tutorials at international conferences.