Creating diverse service configurations that can be swiftly swapped is the essence of the so called Moving Target Defense: presenting a different attack surface for attackers profiling a system for further advances ca...
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When evolutionary algorithms (EAs) are unlikely to locate precise global optimal solutions with satisfactory performances, it is important to substitute alternative theoretical routine for the analysis of hitting time...
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evolutionary algorithms (EAs) and other randomized search heuristics are often considered as unbiased algorithms that are invariant with respect to different transformations of the underlying search space. However, if...
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Concurrency is a powerful abstraction that can be used to model and implement multi-deme evolutionary algorithms, opening up additional design questions such as what the different populations in various threads can do...
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This work presents a new hybrid method based on the route-first-cluster-second approach using Greedy Randomized Adaptive Search Procedure (GRASP), Differential Evolution (DE), evolutionary Local Search (ELS) and set-p...
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This paper proposes an approach to identify relevant parameter combinations within Logical Scenarios for the verification and validation (VV) of automated driving systems (ADS). One approach to potentially reach the g...
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In the evolutionary computation community, it is widely believed that stagnation impedes convergence in evolutionary algorithms, and that convergence inherently indicates optimality. However, this perspective is misle...
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Parallel evolutionary algorithms (PEAs) have been studied for reducing the execution time of evolutionary algorithms by utilizing parallel computing. An asynchronous PEA (APEA) is a scheme of PEAs that increases compu...
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It is natural to think of evolutionary algorithms as highly stochastic search methods. This can also make evolutionary algorithms, and particularly recombination, quite difficult to analyze. One way to reduce randomne...
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ISBN:
(纸本)9781450371285
It is natural to think of evolutionary algorithms as highly stochastic search methods. This can also make evolutionary algorithms, and particularly recombination, quite difficult to analyze. One way to reduce randomness involves the quadratization of functions, which is commonly used by modern optimization methods, and also has applications in quantum computing. After a function is made quadratic, random mutation is obsolete and unnecessary;the location of improving moves can be calculated deterministically, on average in O(1) time. Seemingly impossible problems, such as the Needle-in-a-Haystack, becomes trivial to solve in quadratic form. One can also provably tunnel, or jump, between local optima and quasilocal optima in O(n) time using deterministic genetic recombination. The talk also explores how removing randomness from evolutionary algorithms might provide new insights into natural evolution. Finally, a form of evolutionary algorithm is proposed where premature convergence is impossible and the evolutionary potential of the population remains open-ended.
Although differential evolution (DE) algorithms perform well on a large variety of complicated optimization problems, only a few theoretical studies are focused on the working principal of DE algorithms. To make the f...
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