132,324 research outputs found
Self-adaptation of Mutation Rates in Non-elitist Populations
The runtime of evolutionary algorithms (EAs) depends critically on their
parameter settings, which are often problem-specific. Automated schemes for
parameter tuning have been developed to alleviate the high costs of manual
parameter tuning. Experimental results indicate that self-adaptation, where
parameter settings are encoded in the genomes of individuals, can be effective
in continuous optimisation. However, results in discrete optimisation have been
less conclusive. Furthermore, a rigorous runtime analysis that explains how
self-adaptation can lead to asymptotic speedups has been missing. This paper
provides the first such analysis for discrete, population-based EAs. We apply
level-based analysis to show how a self-adaptive EA is capable of fine-tuning
its mutation rate, leading to exponential speedups over EAs using fixed
mutation rates.Comment: To appear in the Proceedings of the 14th International Conference on
Parallel Problem Solving from Nature (PPSN
Improved Runtime Bounds for the Univariate Marginal Distribution Algorithm via Anti-Concentration
Unlike traditional evolutionary algorithms which produce offspring via
genetic operators, Estimation of Distribution Algorithms (EDAs) sample
solutions from probabilistic models which are learned from selected
individuals. It is hoped that EDAs may improve optimisation performance on
epistatic fitness landscapes by learning variable interactions. However, hardly
any rigorous results are available to support claims about the performance of
EDAs, even for fitness functions without epistasis. The expected runtime of the
Univariate Marginal Distribution Algorithm (UMDA) on OneMax was recently shown
to be in by Dang and Lehre
(GECCO 2015). Later, Krejca and Witt (FOGA 2017) proved the lower bound
via an involved drift analysis.
We prove a bound, given some restrictions
on the population size. This implies the tight bound when , matching the runtime
of classical EAs. Our analysis uses the level-based theorem and
anti-concentration properties of the Poisson-Binomial distribution. We expect
that these generic methods will facilitate further analysis of EDAs.Comment: 19 pages, 1 figur
Investigating possible causal relations among physical, chemical and biological variables across regions in the Gulf of Maine
We examine potential causal relations between ecosystem variables in four regions of the Gulf of Maine under two major assumptions: (i) a causal cyclic variable will precede, or lead, its effect variable; e.g., a peak (through) in the causal variable will come before a peak (through) in the effect variable. (ii) If physical variables determine regional ecosystem properties, then independent clusters of observations of physical, biological and interaction variables from the same stations will show similar patterns. We use the leadingâlagging-strength method to establish leading strength and potential causality, and we use principal component analysis, to establish if regions differ in their ecological characteristics. We found that several relationships for physical and chemical variables were significant, and consistent with ââcommon knowledgeââ of causal relations. In contrast, relationships that included biological variables differed among regions. In spite of these findings, we found that physical and chemical characteristics of near shore and pelagic regions of the Gulf of Maine translate into unique biological assemblages and unique physicalâbiologi- cal interaction
A Parameterized Complexity Analysis of Bi-level Optimisation with Evolutionary Algorithms
Bi-level optimisation problems have gained increasing interest in the field
of combinatorial optimisation in recent years. With this paper, we start the
runtime analysis of evolutionary algorithms for bi-level optimisation problems.
We examine two NP-hard problems, the generalised minimum spanning tree problem
(GMST), and the generalised travelling salesman problem (GTSP) in the context
of parameterised complexity.
For the generalised minimum spanning tree problem, we analyse the two
approaches presented by Hu and Raidl (2012) with respect to the number of
clusters that distinguish each other by the chosen representation of possible
solutions. Our results show that a (1+1) EA working with the spanning nodes
representation is not a fixed-parameter evolutionary algorithm for the problem,
whereas the global structure representation enables to solve the problem in
fixed-parameter time. We present hard instances for each approach and show that
the two approaches are highly complementary by proving that they solve each
other's hard instances very efficiently.
For the generalised travelling salesman problem, we analyse the problem with
respect to the number of clusters in the problem instance. Our results show
that a (1+1) EA working with the global structure representation is a
fixed-parameter evolutionary algorithm for the problem
Theravada Buddhismus aus feministischer Perspektive
1. Die Lehre des Buddhismus ist die Lehre von Ursache und Wirkung. Buddha sagt:"Wer das bedingte Entstehen versteht, versteht Dharma, wer den Dharma versteht, versteht das bedingte Entstehen". Dharma ist die Lehre des Buddha. Dharma bedeutet "Wahrheit", "GesetzmĂ€Ăigkeit", "Naturgesetzt". Die gesamte Lehre von Buddha handelt von Menschen, von uns und von der Natur. Buddha hat ein andermal gesagt: "Die Lehre ĂŒber das Entstehen in AbhĂ€ngigkeit ist sehr tiefgrĂŒndig und subtil". Nur mit dem Intellekt können wir es nicht "verstehen." Wörter sind leider nur ein lineares intellektuelles Mittel, was begrenzt ist...
Das Internet in der Biologielehrerausbildung - ein Zwischenbericht
Auch in der universitĂ€ren Lehre nimmt die Bedeutung des Internet stĂ€ndig zu. Im Beitrag werden verschiedene Möglichkeiten beschrieben, wie das world wide web schon heute in der Biologielehrerausbildung eingesetzt wird. Vorgestellt werden internetunterstĂŒtzte Lehre und verschiedene Formen der internetgestĂŒtzten Lehre, wie Teleteaching, Expertensysteme, virtuelle Seminare und WBT
New insights on neutral binary representations for evolutionary optimization
This paper studies a family of redundant binary representations NNg(l, k), which are based on the mathematical formulation of error control codes, in particular, on linear block codes, which are used to add redundancy and neutrality to the representations. The analysis of the properties of uniformity, connectivity, synonymity, locality and topology of the NNg(l, k) representations is presented, as well as the way an (1+1)-ES can be modeled using Markov chains and applied to NK fitness landscapes with adjacent neighborhood.The results show that it is possible to design synonymously redundant representations that allow an increase of the connectivity between phenotypes. For easy problems, synonymously NNg(l, k) representations, with high locality, and where it is not necessary to present high values of connectivity are the most suitable for an efficient evolutionary search. On the contrary, for difficult problems, NNg(l, k) representations with low locality, which present connectivity between intermediate to high and with intermediate values of synonymity are the best ones. These results allow to conclude that NNg(l, k) representations with better performance in NK fitness landscapes with adjacent neighborhood do not exhibit extreme values of any of the properties commonly considered in the literature of evolutionary computation. This conclusion is contrary to what one would expect when taking into account the literature recommendations. This may help understand the current difficulty to formulate redundant representations, which are proven to be successful in evolutionary computation. (C) 2016 Elsevier B.V. All rights reserved
Parallel black-box complexity with tail bounds
We propose a new black-box complexity model for search algorithms evaluating λ search points in parallel. The parallel unary unbiased black-box complexity gives lower bounds on the number of function evaluations every parallel unary unbiased black-box algorithm needs to optimise a given problem. It captures the inertia caused by offspring populations in evolutionary algorithms and the total computational effort in parallel metaheuristics. We present complexity results for LeadingOnes and OneMax. Our main result is a general performance limit: we prove that on every function every λ-parallel unary unbiased algorithm needs at least a certain number of evaluations (a function of problem size and λ) to find any desired target set of up to exponential size, with an overwhelming probability. This yields lower bounds for the typical optimisation time on unimodal and multimodal problems, for the time to find any local optimum, and for the time to even get close to any optimum. The power and versatility of this approach is shown for a wide range of illustrative problems from combinatorial optimisation. Our performance limits can guide parameter choice and algorithm design; we demonstrate the latter by presenting an optimal λ-parallel algorithm for OneMax that uses parallelism most effectively
- âŠ