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Evolutionary computation [PDF]
Nature Reviews Genetics, 2001 Evolution does not require DNA, or even living organisms. In computer science, the field known as 'evolutionary computation' uses evolution as an algorithmic tool, implementing random variation, reproduction and selection by altering and moving data within a computer.
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Evolutionary computation and cryptology [PDF]
Proceedings of the Genetic and Evolutionary Computation Conference Companion, 2016 Evolutionary Computation (EC) has been used with great success on various real-world problems. One domain abundant with numerous difficult problems is cryptology. Cryptology can be divided into cryptography, that informally speaking considers methods how to ensure secrecy (but also authenticity, privacy, etc.), and cryptanalysis, that deals with ...
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Computational complexity and evolutionary computation
Proceedings of the 9th annual conference companion on Genetic and evolutionary computation, 2007Evolutionary algorithms and other nature-inspired search heuristics like ant colony optimization have been shown to be very successful when dealing with real-world applications or problems from combinatorial optimization. In recent years, analyses has shown that these general randomized search heuristics can be analyzed like "ordinary" randomized ...
Thomas Jansen, Frank Neumann
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Computational Evolutionary Perception
Perception, 2012Marr proposed that human vision constructs “a true description of what is there”. He argued that to understand human vision one must discover the features of the world it recovers and the constraints it uses in the process. Bayesian decision theory (BDT) is used in modern vision research as a probabilistic framework for understanding human vision ...
Donald D. Hoffman, Manish Singh
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Evolutionary computation and games
Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion, 2016In recent years, the field of Computational Intelligence and Games (CIG) has enjoyed rapid progress and a sharp rise in popularity. In this field, algorithms from across the computational intelligence spectrum are tested on benchmarks based on e.g. board games and video games, and new CI-based solutions are developed for problems in game development ...
Georgios N. Yannakakis +2 more
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Scalable Computing for Evolutionary Genomics [PDF]
, 2012 Genomic data analysis in evolutionary biology is becoming so computationally intensive that analysis of multiple hypotheses and scenarios takes too long on a single desktop computer. In this chapter, we discuss techniques for scaling computations through parallelization of calculations, after giving a quick overview of advanced programming techniques ...
Steffen Möller +4 more
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Topics in Evolutionary Computation [PDF]
, 2000 Abstract : This project contributed new principles for the development of intelligent, mobile robots performing complex tasks in unpredictable environments. In the behavior-based approach to robot design, the overall performance of the robot arises through the interaction of multiple, relatively simple, behaviors.
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Evolutionary Computation for Evolutionary Theory
2003Evolutionary computation has developed primarily as an engineering tool for finding solutions to difficult problems. In the shadow of this success, a revolution has been building in evolutionary theory. Evolutionary models, often quite similar to genetic algorithms, are being used to extend our theoretical understanding of biology.
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2018
Evolutionary computation is regarded as significant research area in computer science and has gained popularity in recent years because of its significant advantages as compared to deterministic methods. Evolutionary computation methods are highly preferred to solve multi-objective optimization problems where objective functions with conflicts are ...
Anand Nayyar +3 more
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Evolutionary computation is regarded as significant research area in computer science and has gained popularity in recent years because of its significant advantages as compared to deterministic methods. Evolutionary computation methods are highly preferred to solve multi-objective optimization problems where objective functions with conflicts are ...
Anand Nayyar +3 more
openaire +2 more sources