This paper wishes to describe evolutionary algorithms as an effective means for the solution of the Aerofoil Design Optimisation in Aerodynamics. Firstly the basic ideas underlying evolutionary algorithms are outlined...
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This paper wishes to describe evolutionary algorithms as an effective means for the solution of the Aerofoil Design Optimisation in Aerodynamics. Firstly the basic ideas underlying evolutionary algorithms are outlined. Several versions of evolutionary algorithms are briefly described, focussing on their similarities and on their differences as well. Then their application to both Direct and Inverse Aerofoil Design Problem is described, and results are given. Finally, several possible parallel models for evolutionary algorithms are discussed, and the results of the application of one of them to the above problem are presented.
One of the goals of computational chemistry is the automated de novo design of bioactive molecules. Despite significant progress in computational approaches to ligand design and efficient evaluation of binding energy,...
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One of the goals of computational chemistry is the automated de novo design of bioactive molecules. Despite significant progress in computational approaches to ligand design and efficient evaluation of binding energy, novel procedures for ligand design are required. evolutionary computation provides a new approach to this design issue. This paper presents an automated methodology for computer-aided peptide design based on evolutionary algorithms. It provides an automatic tool for peptide de novo design, based on protein surface patches defined by user. Regarding the restrictive constrains of this problem a special emphasis has been made on the design of the evolutionary algorithms implemented.
In the last years, several real-world problems that require to optimise an increasing number of variables have appeared. This type of optimisation, called large-scale global optimisation, is hard due to the huge incre...
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In the last years, several real-world problems that require to optimise an increasing number of variables have appeared. This type of optimisation, called large-scale global optimisation, is hard due to the huge increase of the domain search due to the dimensionality. Large-scale global optimisation is a research area getting more attention in the last years, thus many algorithms, mainly evolutionary algorithms, have been specially designed to tackle it. In this paper, we give a brief introduction of several of them and their techniques, remarking techniques based on grouping of variables and memetic algorithms, because they are two promising approaches. Also, we have reviewed the winners of the different competitions in the area, to give a snapshot of the algorithms that have obtained the best results in this area. Finally, several interesting trends in the research area have been pointed out, and some future trends and challenges have been suggested.
We analyze the performance of evolutionary algorithms on various matroid optimization problems that encompass a vast number of efficiently solvable as well as NP-hard combinatorial optimization problems (including man...
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We analyze the performance of evolutionary algorithms on various matroid optimization problems that encompass a vast number of efficiently solvable as well as NP-hard combinatorial optimization problems (including many well-known examples such as minimum spanning tree and maximum bipartite matching). We obtain very promising bounds on the expected running time and quality of the computed solution. Our results establish a better theoretical understanding of why randomized search heuristics yield empirically good results for many real-world optimization problems.
This paper considers the problem of scheduling part families and jobs within each part family in a flowshop manufacturing cell with sequence dependent family setups times where it is desired to minimize the makespan w...
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This paper considers the problem of scheduling part families and jobs within each part family in a flowshop manufacturing cell with sequence dependent family setups times where it is desired to minimize the makespan while processing parts (jobs) in each family together. Two evolutionary algorithms-a Genetic Algorithm and a Memetic Algorithm with local search-are proposed and empirically evaluated as to their effectiveness in finding optimal permutation schedules. The proposed algorithms use a compact representation for the solution and a hierarchically structured population where the number of possible neighborhoods is limited by dividing the population into clusters. In comparison to a Multi-Start procedure, solutions obtained by the proposed evolutionary algorithms were very close to the lower bounds for all problem instances. Moreover, the comparison against the previous best algorithm, a heuristic named CMD, indicated a considerable performance improvement. (c) 2005 Elsevier Ltd. All rights reserved.
In this paper the method of selecting a representative subset of Pareto optimal solutions is used to make the search of Pareto frontier more effective. Firstly, the evolutionary algorithm method for generating a set o...
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ISBN:
(纸本)3540417451
In this paper the method of selecting a representative subset of Pareto optimal solutions is used to make the search of Pareto frontier more effective. Firstly, the evolutionary algorithm method for generating a set of Pareto optimal solutions is described. Then, indiscernibility interval method is applied to select representative subset of Pareto optimal solutions. The main idea of this method consists in removing from the set of Pareto optimal solutions these solutions, which are close to each other in the space of objectives, i.e., those solutions for which the values of the objective functions differ less than an indiscernibility interval. The set of Pareto optimal solutions is reduced using indiscernibility interval method after running a certain number of generations. This process can be called the filtration process in which less important Pareto optimal solutions are removed from the existing set. Finally, two design optimization problems are solved using the proposed method. From these examples it is clear that the computation time can be reduced significantly and still the real Pareto frontier obtained.
Path planning for an underwater vehicle ran be formulated as a multiobjective optimization problem, which can be solved by modern heuristic techniques. For assessment of a trajectory, three crucial criteria are used: ...
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ISBN:
(纸本)8371435150
Path planning for an underwater vehicle ran be formulated as a multiobjective optimization problem, which can be solved by modern heuristic techniques. For assessment of a trajectory, three crucial criteria are used: a total length of a path, a smoothness of a trajectory, and a measure of safety. A multiobjective evolutionary algorithm for finding Pareto-optimal solutions is proposed. Then, the underwater vehicle and navigation in three dimensions is considered. Some results of numerical simulations are presented.
Vectorial Genetic Programming (Vec-GP) extends regular GP by allowing vectorial input features (e.g. time series data), while retaining the expressiveness and interpretability of regular GP. The availability of raw ve...
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ISBN:
(纸本)9798400701207
Vectorial Genetic Programming (Vec-GP) extends regular GP by allowing vectorial input features (e.g. time series data), while retaining the expressiveness and interpretability of regular GP. The availability of raw vectorial data during training, not only enables Vec-GP to select appropriate aggregation functions itself, but also allows Vec-GP to extract segments from vectors prior to aggregation (like windows for time series data). This is a critical factor in many machine learning applications, as vectors can be very long and only small segments may be relevant. However, allowing aggregation over segments within GP models makes the training more complicated. We explore the use of common evolutionary algorithms to help GP identify appropriate segments, which we analyze using a simplified problem that focuses on optimizing aggregation segments on fixed data. Since the studied algorithms are to be used in GP for local optimization (e.g. as mutation operator), we evaluate not only the quality of the solutions, but also take into account the convergence speed and anytime performance. Among the evaluated algorithms, CMA-ES, PSO and ALPS show the most promising results, which would be prime candidates for evaluation within GP.
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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ISBN:
(纸本)9783030581114;9783030581121
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 a certain amount of domain knowledge is available the use of biased search operators in EAs becomes viable. We consider a simple (1+1) EA for binary search spaces and analyze an asymmetric mutation operator that can treat zero- and one-bits differently. This operator extends previous work by Jansen and Sudholt (ECJ 18(1), 2010) by allowing the operator asymmetry to vary according to the success rate of the algorithm. Using a self-adjusting scheme that learns an appropriate degree of asymmetry, we show improved runtime results on the class of functions OneMaxa describing the number of matching bits with a fixed target alpha is an element of{0, 1}(n).
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