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Heavy-Tailed Mutation Operators in Single-Objective Combinatorial Optimization [chapter]

Tobias Friedrich, Andreas Göbel, Francesco Quinzan, Markus Wagner
2018 Lecture Notes in Computer Science  
In comparison with uniform mutation and a recently proposed dynamic scheme our operator comes out on top on these instances.  ...  We show that the (1+1) EA using our mutation operator finds a (1/3)approximation ratio on any non-negative submodular function in polynomial time.  ...  This operator performs well in optimizing pseudo-Boolean functions, as well as combinatorial problems such as the minimum vertex cover and the maximum cut.  ... 
doi:10.1007/978-3-319-99253-2_11 fatcat:o6b37dvpufajdleslsrp7yakvy

Specific Single- and Multi-Objective Evolutionary Algorithms for the Chance-Constrained Knapsack Problem [article]

Yue Xie, Aneta Neumann, Frank Neumann
2020 arXiv   pre-print
We examine the use of heavy-tail mutations and introduce a problem-specific crossover operator to deal with the chance-constrained knapsack problem.  ...  In this paper, consider problem-specific single-objective and multi-objective approaches for the problem.  ...  Compare to the standard mutation operator, the heavy-tail mutation operator can flip more than one bit in each step, and it has shown to be useful in some singleobjective combinatorial optimization problems  ... 
arXiv:2004.03205v2 fatcat:c6nlrbisebgj3nzyrnyyrknsom

Coevolutionary Pareto Diversity Optimization [article]

Aneta Neumann, Denis Antipov, Frank Neumann
2022 arXiv   pre-print
In this paper, we introduce a coevolutionary Pareto Diversity Optimization approach which builds on the success of reformulating a constrained single-objective optimization problem as a bi-objective problem  ...  Computing diverse sets of high quality solutions for a given optimization problem has become an important topic in recent years.  ...  This leads us to the conclusion that there is no benefit of the used heavy tail mutation operator.  ... 
arXiv:2204.05457v1 fatcat:rl66avdm4jfarexf6d42ysfmfm

Coevolutionary Pareto diversity optimization

Aneta Neumann, Denis Antipov, Frank Neumann
2022 Proceedings of the Genetic and Evolutionary Computation Conference  
In this paper, we introduce a coevolutionary Pareto Diversity Optimization approach which builds on the success of reformulating a constrained single-objective optimization problem as a bi-objective problem  ...  Computing diverse sets of high quality solutions for a given optimization problem has become an important topic in recent years.  ...  This leads us to the conclusion that there is no benefit the heavy tail mutation operator.  ... 
doi:10.1145/3512290.3528755 fatcat:6jywo2gxifho3jsadgxcboctdy

Fast Genetic Algorithms [article]

Benjamin Doerr, Huu Phuoc Le, Régis Makhmara, Ta Duy Nguyen
2017 arXiv   pre-print
We prove that the (1+1) EA with this heavy-tailed mutation rate optimizes any _m,n function in a time that is only a small polynomial (in m) factor above the one stemming from the optimal rate for this  ...  Our heavy-tailed mutation operator yields similar speed-ups (over the best known performance guarantees) for the vertex cover problem in bipartite graphs and the matching problem in general graphs.  ...  Acknowledgements This research was supported by Labex DigiCosme (project ANR11LABEX0045DIGICOSME) operated by ANR as part of the program "Investissement d'Avenir" Idex ParisSaclay (ANR11IDEX000302) as  ... 
arXiv:1703.03334v2 fatcat:2rjgzqs52fbgholexxudmwnrge

Theoretical Analyses of Multi-Objective Evolutionary Algorithms on Multi-Modal Objectives

Benjamin Doerr, Weijie Zheng
2021 PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE  
To improve the performance, we combine the GSEMO with two approaches, a heavy-tailed mutation operator and a stagnation detection strategy, that showed advantages in single-objective multi-modal problems  ...  Overall, these results show that the ideas recently developed for single-objective evolutionary algorithms can be effectively employed also in multi-objective optimization.  ...  GSEMO with Heavy-Tailed Mutation In the previous section, we have shown that the GSEMO can optimize our multi-modal optimization problem, but similar to the single-objective world (say, the optimization  ... 
doi:10.1609/aaai.v35i14.17459 fatcat:sezfyjharndqtnzll37xqh6hvq

A gentle introduction to theory (for non-theoreticians)

Benjamin Doerr
2022 Proceedings of the Genetic and Evolutionary Computation Conference Companion  
Major contributions to the latter include runtime analyses for existing evolutionary algorithms, the determination of optimal parameter values, and the theory-guided design of novel operators, on-the-fly  ...  Together with Frank Neumann, he edited the book Theory of Evolutionary Computation -Recent Developments in Discrete Optimization (Springer 2020). 2 Benjamin Doerr: Introduction to theory (tutorial) This  ...  to theory (tutorial)Heavy-tailed Mutation: Results Theorem: The (1+1) EA with heavy-tailed mutation ( ) has an expected optimization time on , of .  ... 
doi:10.1145/3520304.3533628 fatcat:5ldlw3k3hfasrildcrrm5uctmq

Optimization Algorithm for Route Selection based on Intelligent Optimization

Wang Aiju, Shao Hua
2015 Open Automation and Control Systems Journal  
When optimizing multiple objective functions with the Gene Expression Programming (GEP) algorithm, the ORS-SAGEP algorithm performs simulated annealing for every individual in the initial population, and  ...  single GEP's problems of poor optimization accuracy and easily falling into local optimum.  ...  Based on the characteristics of route optimization genes, and to ensure that the genes obtained are valid, the entire mutation operation can only be carried out between the head and tail of the route optimization  ... 
doi:10.2174/1874444301507010314 fatcat:pu76xkvojjc6jp7bkque3mg7ri

A novel quantum inspired cuckoo search for knapsack problems

Abdesslem Layeb
2011 International Journal of Bio-Inspired Computation (IJBIC)  
The second contribution is proposition of a new hybrid quantum measure operation which uses first fit heuristic to pack no filled objects by the standard measure operation.  ...  The Bin Packing Problem (BPP) is one of the most known combinatorial optimization problems. This problem consists to pack a set of items into a minimum number of bins.  ...  Introduction The combinatorial optimization plays a very important role in operational research, discrete mathematics and computer science.  ... 
doi:10.1504/ijbic.2011.042260 fatcat:2vmly6cj5ng2pky7tlmb545i3y

A Novel Quantum Inspired Cuckoo Search Algorithm for Bin Packing Problem

Abdesslem Layeb, Seriel Rayene Boussalia
2012 International Journal of Information Technology and Computer Science  
The second contribution is proposition of a new hybrid quantum measure operation which uses first fit heuristic to pack no filled objects by the standard measure operation.  ...  The Bin Packing Problem (BPP) is one of the most known combinatorial optimization problems. This problem consists to pack a set of items into a minimum number of bins.  ...  Introduction The combinatorial optimization plays a very important role in operational research, discrete mathematics and computer science.  ... 
doi:10.5815/ijitcs.2012.05.08 fatcat:klbxh4zdijdztbhypf2twxqssa

An Improved Clustering Based Genetic Algorithm for Solving Complex NP Problems

Sivaraj
2011 Journal of Computer Science  
The selection pressure is the critical step which finds out the best individuals in the entire population for further genetic operators.  ...  The fit individuals are selected for crossover and mutation in all generations thereby reaching the solution without much complex process.  ...  Genetic recombination operators: Although many genetic recombination operators are available, the commonly used ones are crossover and mutation Fig. 1 and 2.  ... 
doi:10.3844/jcssp.2011.1033.1037 fatcat:mthoevzd2fejxeczszjpez7lom

Theoretical Analyses of Multi-Objective Evolutionary Algorithms on Multi-Modal Objectives [article]

Benjamin Doerr, Weijie Zheng
2021 arXiv   pre-print
To improve the performance, we combine the GSEMO with two approaches, a heavy-tailed mutation operator and a stagnation detection strategy, that showed advantages in single-objective multi-modal problems  ...  Overall, these results show that the ideas recently developed for single-objective evolutionary algorithms can be effectively employed also in multi-objective optimization.  ...  Acknowledgments This work was supported by a public grant as part of the Investissement d'avenir project, reference ANR-11-LABX-0056-LMH, LabEx LMH, in a joint call with Gaspard Monge Program for optimization  ... 
arXiv:2012.07231v3 fatcat:vuwgaxphfben5jot5qohnxgm5u

A novel greedy quantum inspired cuckoo search algorithm for variable sized bin packing problem

Abdesslem Layeb, Seriel Rayene Boussalia
2014 International Journal of Mathematics in Operational Research (IJMOR)  
The second contribution is a proposition of a new hybrid quantum measure operation which uses first fit heuristic to pack no filled objects by the standard measure operation.  ...  The objective is to pack all the items in the bins minimizing the sum of the remaining spaces of the used bins.  ...  INTRODUCTION The combinatorial optimization plays a very important role in operational research, discrete mathematics and computer science.  ... 
doi:10.1504/ijmor.2014.065420 fatcat:hfgvwiebzzcrjlfxzvsqjer7xu

Quantum inspired cuckoo search algorithm for graph colouring problem

Halima Djelloul, Abdesslem Layeb, Salim Chikhi
2015 International Journal of Bio-Inspired Computation (IJBIC)  
The graph coloring problem (GCP) is one of the most interesting, studied and difficult combinatorial optimization problems.  ...  The second contribution is the proposition of a novel measure operator based on the adjacency matrix. The third contribution involves the proposition of an adapted hybrid quantum mutation operation.  ...  Here the consecutive jumps/steps of a cuckoo essentially form arandom walk process which obeys a power-law step length distribution with a heavy tail [17] . x i t+1 = x i t + α ⊕ Lévy(λ) (5) Lévy ~ u=  ... 
doi:10.1504/ijbic.2015.069554 fatcat:urza3pid7fdvxfo7an2j5hrsbu

Stagnation Detection Meets Fast Mutation [article]

Benjamin Doerr, Amirhossein Rajabi
2022 arXiv   pre-print
Two mechanisms have recently been proposed that can significantly speed up finding distant improving solutions via mutation, namely using a random mutation rate drawn from a heavy-tailed distribution (  ...  In this work, we propose a mutation strategy that combines ideas of both mechanisms. We show that it can also obtain the best possible probability of finding a single distant solution.  ...  Various variants of heavy-tailed mutation operators have been proposed subsequently, also heavy-tailed choices of other parameters have been used with great success [FQW18,FGQW18b,FGQW18a, WQT18, ABD20a  ... 
arXiv:2201.12158v2 fatcat:hvmrc5bburhnlenm47m6w2c2p4
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