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Combining convergence and diversity in evolutionary multiobjective optimization
 Evolutionary Computation
, 2002
"... Over the past few years, the research on evolutionary algorithms has demonstrated their niche in solving multiobjective optimization problems, where the goal is to �nd a number of Paretooptimal solutions in a single simulation run. Many studies have depicted different ways evolutionary algorithms c ..."
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Cited by 156 (15 self)
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Over the past few years, the research on evolutionary algorithms has demonstrated their niche in solving multiobjective optimization problems, where the goal is to �nd a number of Paretooptimal solutions in a single simulation run. Many studies have depicted different ways evolutionary algorithms can progress towards the Paretooptimal set with a widely spread distribution of solutions. However, none of the multiobjective evolutionary algorithms (MOEAs) has a proof of convergence to the true Paretooptimal solutions with a wide diversity among the solutions. In this paper, we discuss why a number of earlier MOEAs do not have such properties. Based on the concept ofdominance, new archiving strategies are proposed that overcome this fundamental problem and provably lead to MOEAs that have both the desired convergence and distribution properties. A number of modi�cations to the baseline algorithm are also suggested. The concept ofdominance introduced in this paper is practical and should make the proposed algorithms useful to researchers and practitioners alike.
A Tutorial on Evolutionary Multiobjective Optimization
 In Metaheuristics for Multiobjective Optimisation
, 2003
"... Mu l ip often conflicting objectives arise naturalj in most real worl optimization scenarios. As evol tionaryalAxjO hms possess several characteristics that are desirabl e for this type of probl em, this clOv of search strategies has been used for mul tiobjective optimization for more than a decade. ..."
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Cited by 75 (0 self)
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Mu l ip often conflicting objectives arise naturalj in most real worl optimization scenarios. As evol tionaryalAxjO hms possess several characteristics that are desirabl e for this type of probl em, this clOv of search strategies has been used for mul tiobjective optimization for more than a decade. Meanwhil e evol utionary mul tiobjective optimization has become establ ished as a separate subdiscipl ine combining the fiel ds of evol utionary computation and cl assical mul tipl e criteria decision ma ing. This paper gives an overview of evol tionary mu l iobjective optimization with the focus on methods and theory. On the one hand, basic principl es of mu l iobjective optimization and evol tionary alA#xv hms are presented, and various al gorithmic concepts such as fitness assignment, diversity preservation, and el itism are discussed. On the other hand, the tutorial incl udes some recent theoretical resul ts on the performance of mu l iobjective evol tionaryalvDfifl hms and addresses the question of how to simpl ify the exchange of methods and appl ications by means of a standardized interface. 1
Running Time Analysis of a MultiObjective Evolutionary Algorithm on a Simple Discrete Optimization Problem
, 2002
"... For the first time, a running time analysis of a multiobjective evolutionary algorithm for a discrete optimization problem is given. To this end, a simple pseudoBoolean problem (Lotz: leading ones  trailing zeroes) is defined and a populationbased optimization algorithm (FEMO). We show, that the ..."
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Cited by 52 (8 self)
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For the first time, a running time analysis of a multiobjective evolutionary algorithm for a discrete optimization problem is given. To this end, a simple pseudoBoolean problem (Lotz: leading ones  trailing zeroes) is defined and a populationbased optimization algorithm (FEMO). We show, that the algorithm performs a black box optimization in #(n 2 log n) function evaluations where n is the number of binary decision variables. 1
Mutation Control And Convergence In Evolutionary MultiObjective Optimization
, 2001
"... This paper addresses the problem of controlling mutation strength in multiobjective evolutionary algorithms and its implications for the convergence to the Pareto set. Adaptive parameter control is one major issue in the field of evolutionary computation, and several methods have been proposed an ..."
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Cited by 15 (4 self)
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This paper addresses the problem of controlling mutation strength in multiobjective evolutionary algorithms and its implications for the convergence to the Pareto set. Adaptive parameter control is one major issue in the field of evolutionary computation, and several methods have been proposed and applied successfully for single objective optimization problems. In this study we examine whether these results carry over to the multiobjective case and what modifications must be taken to meet the difficulties and pitfalls of conflicting objectives. 1
Running Time Analysis of Evolutionary Algorithms on VectorValued PseudoBoolean Functions
 IEEE Trans. Evolutionary Comput
, 2003
"... This paper presents a rigorous running time analysis of evolutionary algorithms on pseudoBoolean multiobjective optimization problems. We propose and analyze dierent populationbased algorithms, the simple evolutionary multiobjective optimizer SEMO and two improved versions, FEMO and GEMO. The ..."
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Cited by 12 (1 self)
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This paper presents a rigorous running time analysis of evolutionary algorithms on pseudoBoolean multiobjective optimization problems. We propose and analyze dierent populationbased algorithms, the simple evolutionary multiobjective optimizer SEMO and two improved versions, FEMO and GEMO. The analysis is carried out on two biobjective model problems, LOTZ (Leading Ones Trailing Zeroes) and COCZ (Count Ones Count Zeroes) as well as on the scalable mobjective versions mLOTZ and mCOCZ. Results on the running time of the dierent populationbased algorithms and for an alternative approach, a multistart (1+1)EA based on the constraint method, are derived The comparison reveals that for many problems, the simple algorithm SEMO is as ecient as the (1+1)EA. For some problems, the improved variants FEMO and GEMO are provably better. For the analysis we propose and apply two general tools, an upper bound technique based on a decision space partition and a randomized graph search algorithm, which facilitate the analysis considerably.
An analysis on recombination in multiobjective evolutionary optimization
 In Proceedings of the 13th ACM Annual Conference on Genetic and Evolutionary Computation (GECCO’11
, 2011
"... Evolutionary algorithms (EAs) are increasingly popular approaches to multiobjective optimization. One of their significant advantages is that they can directly optimize the Pareto front by evolving a population of solutions, where the recombination (also called crossover) operators are usually emp ..."
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Cited by 8 (5 self)
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Evolutionary algorithms (EAs) are increasingly popular approaches to multiobjective optimization. One of their significant advantages is that they can directly optimize the Pareto front by evolving a population of solutions, where the recombination (also called crossover) operators are usually employed to reproduce new and potentially better solutions by mixing up solutions in the population. Recombination in multiobjective evolutionary algorithms is, however, mostly applied heuristically. In this paper, we investigate how from a theoretical viewpoint a recombination operator will affect a multiobjective EA. First, we employ artificial benchmark problems: the Weighted LPTNO problem (a generalization of the wellstudied LOTZ problem), and the wellstudied COCZ problem, for studying the effect of recombination. Our analysis discloses that recombination may accelerate the filling of the Pareto front by recombining diverse solutions and thus help solve multiobjective optimization. Because of this, for these two problems, we find that a multiobjective EA with recombination enabled achieves a better expected running time than any known EAs with recombination disabled. We further examine the effect of recombination on solving the multiobjective minimum spanning tree problem, which is an NPHard problem. Following our finding on the artificial problems, our analysis shows that recombination also helps accelerate filling the Pareto front and thus helps find approximate solutions faster.
ETEA: A Euclidean Minimum Spanning TreeBased Evolutionary Algorithm for MultiObjective Optimization
"... Evolutionary Computation corrected proof doi:10.1162/EVCO_a_00106 ..."
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Evolutionary Computation corrected proof doi:10.1162/EVCO_a_00106
Towards a Tactile Communication System with Dialogbased Tuning
"... Abstract — We present a tactile intelligent sensory substitution system (TIS 3) as a novel tactile communication system with dialogbased tuning. TIS 3 consists of a tactile encoder (TE) which maps desired objects or patterns (P) onto spatiotemporal stimulation patterns (P’) as a parallel stream of ..."
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Abstract — We present a tactile intelligent sensory substitution system (TIS 3) as a novel tactile communication system with dialogbased tuning. TIS 3 consists of a tactile encoder (TE) which maps desired objects or patterns (P) onto spatiotemporal stimulation patterns (P’) as a parallel stream of stimulation time courses, by means of a tactile stimulator array (TS) at selected skin location to elicit tactile perceptions (P ∗). The human subject evaluates and compares a percept P ∗ with a given object P as association goal. A Learning Module (LM) for dialog based TEtuning transforms these evaluations into TEchange signals. In a first step, the application of a dialogbased TEtuning was successfully tested using a TS version with fifteen stimulators on the lower arm for regaining the corresponding stimulation pattern P ’ for a given tactile reference percept P ∗ ref. Optimization of P ∗ was achieved in less than 100 iteration steps by means of a micro evolutionary learning algorithm (MEA). I.
The Neighbor Search approach
, 2012
"... applied to reservoir optimal operation: the Hoa Binh case study Candidato: ..."
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applied to reservoir optimal operation: the Hoa Binh case study Candidato: