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Multiobjective evolutionary algorithms: a comparative case study and the strength pareto approach
 IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION
, 1999
"... Evolutionary algorithms (EA’s) are often wellsuited for optimization problems involving several, often conflicting objectives. Since 1985, various evolutionary approaches to multiobjective optimization have been developed that are capable of searching for multiple solutions concurrently in a singl ..."
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Cited by 781 (22 self)
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Evolutionary algorithms (EA’s) are often wellsuited for optimization problems involving several, often conflicting objectives. Since 1985, various evolutionary approaches to multiobjective optimization have been developed that are capable of searching for multiple solutions concurrently in a single run. However, the few comparative studies of different methods presented up to now remain mostly qualitative and are often restricted to a few approaches. In this paper, four multiobjective EA’s are compared quantitatively where an extended 0/1 knapsack problem is taken as a basis. Furthermore, we introduce a new evolutionary approach to multicriteria optimization, the Strength Pareto EA (SPEA), that combines several features of previous multiobjective EA’s in a unique manner. It is characterized by a) storing nondominated solutions externally in a second, continuously updated population, b) evaluating an individual’s fitness dependent on the number of external nondominated points that dominate it, c) preserving population diversity using the Pareto dominance relationship, and d) incorporating a clustering procedure in order to reduce the nondominated set without destroying its characteristics. The proofofprinciple results obtained on two artificial problems as well as a larger problem, the synthesis of a digital hardware–software multiprocessor system, suggest that SPEA can be very effective in sampling from along the entire Paretooptimal front and distributing the generated solutions over the tradeoff surface. Moreover, SPEA clearly outperforms the other four multiobjective EA’s on the 0/1 knapsack problem.
SelfAdaptation in Genetic Algorithms
 Proceedings of the First European Conference on Artificial Life
, 1992
"... Within Genetic Algorithms (GAs) the mutation rate is mostly handled as a global, external parameter, which is constant over time or exogeneously changed over time. In this paper a new approach is presented, which transfers a basic idea from Evolution Strategies (ESs) to GAs. Mutation rates are chang ..."
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Cited by 127 (2 self)
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Within Genetic Algorithms (GAs) the mutation rate is mostly handled as a global, external parameter, which is constant over time or exogeneously changed over time. In this paper a new approach is presented, which transfers a basic idea from Evolution Strategies (ESs) to GAs. Mutation rates are changed into endogeneous items which are adapting during the search process. First experimental results are presented, which indicate that environment dependent selfadaptation of appropriate settings for the mutation rate is possible even for GAs. Furthermore, the reduction of the number of external parameters of a GA is seen as a first step towards achieving a problemdependent selfadaptation of the algorithm. Introduction Natural evolution has proven to be a powerful mechanism for emergence and improvement of the living beings on our planet by performing a randomized search in the space of possible DNAsequences. Due to this knowledge about the qualities of natural evolution, some resea...
The Equation for the Response to Selection and Its Use for Prediction
, 1997
"... The Breeder Genetic Algorithm (BGA) was designed according to the theories and methods used in the science of livestock breeding. The prediction of a breeding experiment is based on the response to selection (RS) equation. This equation relates the change in a population 's fitness to the stand ..."
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Cited by 118 (15 self)
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The Breeder Genetic Algorithm (BGA) was designed according to the theories and methods used in the science of livestock breeding. The prediction of a breeding experiment is based on the response to selection (RS) equation. This equation relates the change in a population 's fitness to the standard deviation of its fitness, as well as to the parameters selection intensity and realized heritability. In this paper the exact RS equation is derived for proportionate selection given an infinite population in linkage equilibrium. In linkage equilibrium the genotype frequencies are the product of the univariate marginal frequencies. The equation contains Fisher's fundamental theorem of natural selection as an approximation. The theorem shows that the response is approximately equal to the quotient of a quantity called additive genetic variance, VA , and the average fitness. We compare Mendelian twoparent recombination with genepool recombination, which belongs to a special class of genetic ...
Systemlevel synthesis using Evolutionary Algorithms
 J. DESIGN AUTOMATION FOR EMBEDDED SYSTEMS
, 1998
"... In this paper, we consider systemlevel synthesis as the problem of optimally mapping a tasklevel specification onto a heterogeneous hardware/software architecture. This problem requires (1) the selection of the architecture (allocation) including general purpose and dedicated processors, ASICs, bu ..."
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Cited by 102 (41 self)
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In this paper, we consider systemlevel synthesis as the problem of optimally mapping a tasklevel specification onto a heterogeneous hardware/software architecture. This problem requires (1) the selection of the architecture (allocation) including general purpose and dedicated processors, ASICs, busses and memories, (2) the mapping of the specification onto the selected architecture in space (binding) and time (scheduling), and (3) the design space exploration with the goal to find a set of implementations that satisfy a number of constraints on cost and performance. Existing methodologies often consider a fixed architecture, perform the binding only, do not reflect the tight interdependency between binding and scheduling, do not consider communication (tasks and resources), or require long runtimes preventing design space exploration, or yield only one implementation with optimal cost. Here, a model is introduced that handles all mentioned requirements and allows the task of systemsynthesis to be specified as an optimization problem. The application and adaptation of an Evolutionary Algorithm to solve the tasks of optimization and design space exploration is described.
Evaluationrelaxation schemes for genetic and evolutionary algorithms
, 2002
"... Genetic and evolutionary algorithms have been increasingly applied to solve complex, large scale search problems with mixed success. Competent genetic algorithms have been proposed to solve hard problems quickly, reliably and accurately. They have rendered problems that were difficult to solve by th ..."
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Cited by 68 (27 self)
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Genetic and evolutionary algorithms have been increasingly applied to solve complex, large scale search problems with mixed success. Competent genetic algorithms have been proposed to solve hard problems quickly, reliably and accurately. They have rendered problems that were difficult to solve by the earlier GAs to be solvable, requiring only a subquadratic number of function evaluations. To facilitate solving largescale complex problems, and to further enhance the performance of competent GAs, various efficiencyenhancement techniques have been developed. This study investigates one such class of efficiencyenhancement technique called evaluation relaxation. Evaluationrelaxation schemes replace a highcost, lowerror fitness function with a lowcost, higherror fitness function. The error in fitness functions comes in two flavors: Bias and variance. The presence of bias and variance in fitness functions is considered in isolation and strategies for increasing efficiency in both cases are developed. Specifically, approaches for choosing between two fitness functions with either differing variance or differing bias values have been developed. This thesis also investigates fitness inheritance as an evaluation
Migration Policies, Selection Pressure, and Parallel Evolutionary Algorithms
"... This paper investigates how the policy used to select migrants and replacements affects the selection pressure in parallel evolutionary algorithms (EAs) with multiple populations. ..."
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Cited by 42 (2 self)
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This paper investigates how the policy used to select migrants and replacements affects the selection pressure in parallel evolutionary algorithms (EAs) with multiple populations.
On Takeover Times in Spatially Structured Populations: Array and Ring
 PROCEEDINGS OF THE SECOND ASIAPACIFIC CONFERENCE ON GENETIC ALGORITHMS AND APPLICATIONS
, 2000
"... The takeover time is the expected number of iterations of some selection method until a population consists entirely of copies of the best individual under the assumption that only one best individual is contained in the initial population. This quantity may be used to assess and compare the `se ..."
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Cited by 32 (1 self)
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The takeover time is the expected number of iterations of some selection method until a population consists entirely of copies of the best individual under the assumption that only one best individual is contained in the initial population. This quantity may be used to assess and compare the `selection pressures' of selection methods used in evolutionary algorithms. Here, the notion is generalized from spatially unstructured to structured populations. Lower bounds are derived for arbitrary connected neighborhood structures, lower and upper bounds for arraylike structures, and an exact closed form expression if the neighborhood structure is a ring.
Fitness uniform selection to preserve genetic diversity
 In Proc. 2002 Congress on Evolutionary Computation (CEC2002
, 2002
"... In evolutionary algorithms, the fitness of a population increases with time by mutating and recombining individuals and by a biased selection of more fit individuals. The right selection pressure is critical in ensuring sufficient optimization progress on the one hand and in preserving genetic diver ..."
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Cited by 30 (3 self)
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In evolutionary algorithms, the fitness of a population increases with time by mutating and recombining individuals and by a biased selection of more fit individuals. The right selection pressure is critical in ensuring sufficient optimization progress on the one hand and in preserving genetic diversity to be able to escape from local optima on the other. We propose a new selection scheme, which is uniform in the fitness values. It generates selection pressure towards sparsely populated fitness regions, not necessarily towards higher fitness, as is the case for all other selection schemes. We show that the new selection scheme can be much more effective than standard selection schemes. 1
Generalisation of the limiting distribution of program sizes in treebased genetic programming and analysis of its effects on bloat
 in GECCO ’07: Proceedings of the 9th Annual Conference on Genetic and Evolutionary
, 2007
"... Abstract. We provide strong theoretical and experimental evidence that standard subtree crossover with uniform selection of crossover points pushes a population of aary GP trees towards a distribution of tree sizes of the form: Pr{n} =(1−apa) an +1 ..."
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Cited by 27 (10 self)
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Abstract. We provide strong theoretical and experimental evidence that standard subtree crossover with uniform selection of crossover points pushes a population of aary GP trees towards a distribution of tree sizes of the form: Pr{n} =(1−apa) an +1