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16
A review of adaptive population sizing schemes in genetic algorithms
 In: Proc. GECCO’05
, 2005
"... This paper reviews the topic of population sizing in genetic algorithms. It starts by revisiting theoretical models which rely on a facetwise decomposition of genetic algorithms, and then moves on to various selfadjusting population sizing schemes that have been proposed in the literature. The pap ..."
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Cited by 28 (4 self)
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This paper reviews the topic of population sizing in genetic algorithms. It starts by revisiting theoretical models which rely on a facetwise decomposition of genetic algorithms, and then moves on to various selfadjusting population sizing schemes that have been proposed in the literature. The paper ends with recommendations for those who design and compare adaptive population sizing schemes for genetic algorithms.
Scalable estimationofdistribution program evolution
 In Genetic and evolutionary computation conference
, 2007
"... I present a new estimationofdistribution approach to program evolution where distributions are not estimated over the entire space of programs. Rather, a novel representationbuilding procedure that exploits domain knowledge is used to dynamically select program subspaces for estimation over. This ..."
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Cited by 7 (0 self)
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I present a new estimationofdistribution approach to program evolution where distributions are not estimated over the entire space of programs. Rather, a novel representationbuilding procedure that exploits domain knowledge is used to dynamically select program subspaces for estimation over. This leads to a system of demes consisting of alternative representations (i.e. program subspaces) that are maintained simultaneously and managed by the overall system. Metaoptimizing semantic evolutionary search (MOSES), a program evolution system based on this approach, is described, and its representationbuilding subcomponent is analyzed in depth. Experimental results are also provided for the overall MOSES procedure that demonstrate good scalability.
Online Population Size Adjusting Using Noise and Substructural Measurements
, 2005
"... This paper proposes an online population size adjustment scheme for genetic algorithms. It utilizes linkagemodelbuilding techniques to calculate the parameters used in facetwise populationsizing models. The methodology is demonstrated using the dependency structure matrix genetic algorithm on a s ..."
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Cited by 5 (3 self)
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This paper proposes an online population size adjustment scheme for genetic algorithms. It utilizes linkagemodelbuilding techniques to calculate the parameters used in facetwise populationsizing models. The methodology is demonstrated using the dependency structure matrix genetic algorithm on a set of boundedlydifficult problems. Empirical results indicate that the proposed method is both efficient and robust. If the initial population size is too large, the proposed method automatically decreases the population size, and thereby yields significant savings in the number of function evaluations required to obtain highquality solutions; if the initial population size is too small, the proposed scheme increases the population size onthefly and thereby avoiding premature convergence. 1
Revisiting evolutionary algorithms with onthefly population size adjustment
 Proc. of the Genetic and Evolutionary Computation (GECCO 2006
"... In an evolutionary algorithm, the population has a very important role as its size has direct implications regarding solution quality, speed, and reliability. Theoretical studies have been done in the past to investigate the role of population sizing in evolutionary algorithms. In addition to those ..."
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Cited by 4 (0 self)
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In an evolutionary algorithm, the population has a very important role as its size has direct implications regarding solution quality, speed, and reliability. Theoretical studies have been done in the past to investigate the role of population sizing in evolutionary algorithms. In addition to those studies, several selfadjusting population sizing mechanisms have been proposed in the literature. This paper revisits the latter topic and pays special attention to the genetic algorithm with adaptive population size (APGA), for which several researchers have claimed to be very effective at autonomously (re)sizing the population. As opposed to those previous claims, this paper suggests a complete opposite view. Specifically, it shows that APGA is not capable of adapting the population size at all. This claim is supported on theoretical grounds and confirmed by computer simulations.
Sensibility of linkage information and effectiveness of estimated distributions. Evolutionary Computation
"... The probabilistic model building performed by estimation of distribution algorithms (EDAs) enables these methods to use advanced techniques of statistics and machine learning for automatic discovery of problem structures. However, in some situations, it may not be possible to completely and accurate ..."
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Cited by 1 (1 self)
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The probabilistic model building performed by estimation of distribution algorithms (EDAs) enables these methods to use advanced techniques of statistics and machine learning for automatic discovery of problem structures. However, in some situations, it may not be possible to completely and accurately identify the whole problem structure by probabilistic modeling due to certain inherent properties of the given problem. In this work, we illustrate one possible cause of such situations with problems consisting of structures with unequal fitness contributions. Based on the illustrative example, we introduce a notion that the estimated probabilisticmodels should be inspected to reveal the effective search directions and further propose a general approach which utilizes a reserved set of solutions to examine the built model for likely inaccurate fragments. Furthermore, the proposed approach is implemented on the extended compact genetic algorithm (ECGA) and experiments are performed on several sets of additively separable problems with different scaling setups. The results indicate that the proposed method can significantly assist ECGA to handle problems comprising structures of disparate fitness contributions and therefore may potentially help EDAs in general to overcome those situations in which the entire problem structure cannot be recognized properly due to the temporal delay of emergence of some promising partial solutions.
Size Adjustment
, 2006
"... In an evolutionary algorithm, the population has a very important role as its size has direct implications regarding solution quality, speed, and reliability. Theoretical studies have been done in the past to investigate the role of population sizing in evolutionary algorithms. In addition to those ..."
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In an evolutionary algorithm, the population has a very important role as its size has direct implications regarding solution quality, speed, and reliability. Theoretical studies have been done in the past to investigate the role of population sizing in evolutionary algorithms. In addition to those studies, several selfadjusting population sizing mechanisms have been proposed in the literature. This paper revisits the latter topic and pays special attention to the genetic algorithm with adaptive population size (APGA), for which several researchers have claimed to be very effective at autonomously (re)sizing the population. As opposed to those previous claims, this paper suggests a complete opposite view. Specifically, it shows that APGA is not capable of adapting the population size at all. This claim is supported on theoretical grounds and confirmed by computer simulations. 1
Practical, Robust, and Scalable BlackBox Optimization with BOA and hBOA
"... Abstract. To design practical blackbox optimizers, one of the primary goals is to minimize the amount of work that must be done by the user while ensuring that a highquality solution will be found quickly and reliably. This paper shows that probabilistic modelbuilding genetic algorithms (PMBGAs) ..."
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Abstract. To design practical blackbox optimizers, one of the primary goals is to minimize the amount of work that must be done by the user while ensuring that a highquality solution will be found quickly and reliably. This paper shows that probabilistic modelbuilding genetic algorithms (PMBGAs) provide a great framework for designing practical and powerful blackbox optimizers. The paper focuses on two algorithms that are among the most powerful PMBGAs: The Bayesian optimization algorithm (BOA) and the hierarchical BOA (hBOA). 1
Introducing Assignment Functions to Bayesian Optimization Algorithms
"... In this paper we improve Bayesian optimization algorithms by introducing proportionate and rankbased assignment functions. A Bayesian optimization algorithm builds a Bayesian network from a selected subpopulation of promising solutions, and this probabilistic model is employed to generate the offs ..."
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In this paper we improve Bayesian optimization algorithms by introducing proportionate and rankbased assignment functions. A Bayesian optimization algorithm builds a Bayesian network from a selected subpopulation of promising solutions, and this probabilistic model is employed to generate the offspring of the next generation. Our method assigns each solution a relative significance based on its fitness, and this information is used in building the Bayesian network model. These assignment functions can improve the quality of the model without performing an explicit selection on the population. Numerical experiments demonstrate the effectiveness of this method compared to a conventional BOA. Key words: evolutionary computation, Bayesian optimization algorithms, assignment functions 1
ABSTRACT COMPETENT PROGRAM EVOLUTION by Moshe Looks
, 2006
"... Heuristic optimization methods are adaptive when they sample problem solutions based on knowledge of the search space gathered from past sampling. Recently, competent evolutionary optimization methods have been developed that adapt via probabilistic modeling of the search space. However, their effec ..."
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Heuristic optimization methods are adaptive when they sample problem solutions based on knowledge of the search space gathered from past sampling. Recently, competent evolutionary optimization methods have been developed that adapt via probabilistic modeling of the search space. However, their effectiveness requires the existence of a compact problem decomposition in terms of prespecified solution parameters. How can we use these techniques to effectively and reliably solve program learning problems, given that program spaces will rarely have compact decompositions? One method is to manually build a problemspecific representation that is more tractable than the general space. But can this process be automated? My thesis is that the properties of programs and program spaces can be leveraged as inductive bias to reduce the burden of manual representationbuilding, leading to competent program evolution. The central contributions of this dissertation are a synthesis of the requirements for competent program evolution, and the design of a procedure, metaoptimizing semantic evolutionary search (MOSES), that meets these requirements. In support of my thesis,
Designing Genetic Algorithm Based on Exploration and Exploitation of Gene Linkage
, 2007
"... Genetic algorithm (GA) is expected to realize black box optimization, which can solve optimization problems based only on the values of objective functions. Efficient building block mixing is essential in genetic algorithms. For simple GAs, it is not an easy task without prior knowledge of a proble ..."
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Genetic algorithm (GA) is expected to realize black box optimization, which can solve optimization problems based only on the values of objective functions. Efficient building block mixing is essential in genetic algorithms. For simple GAs, it is not an easy task without prior knowledge of a problem and such knowledge is not always available. GAs which can learn or detect problem structure automatically are called competent genetic algorithms (cGAs). This dissertation proposes two important parts to realize cGAs, (1) a novel approach to identify linkages and (2) a crossover for functions with complexly overlapping building blocks. First, we propose a novel linkage identification method called Dependency Detection for Distribution Derived from fitness Differences (D5), which detects linkage by estimating strings clustered according to fitness differences caused by perturbations. It is important to detect linkage — interaction between variables tightly linked to form a building block — to process building blocks effectively. The D5 inherits the merits of two classes of cGAs, estimation of distribution algorithms (EDAs) and perturbation methods (PMs), that is, it can detect linkages for problems which are difficult for EDAs requiring smaller computational cost than PMs. In addition, ContextDependent Crossover (CDC) has been developed to combine complexly overlapping building blocks. The CDC examine contexts of each pair of strings in addition to the linkage information to process building blocks. Combining the linkage identification and the crossover methods, we have realized a competent genetic algorithm applicable to widerspectrum realworld problems.