NDLI: Investigating and exploiting the bias of the weighted hypervolume to articulate user preferences

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Competitive simulated annealing and Tabu Search algorithms for the max-cut problem

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Investigating and exploiting the bias of the weighted hypervolume to articulate user preferences

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Hybrid evolutionary algorithms for large scale continuous problems

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Particle swarm optimization with information share mechanism

The effect of vesicular selection in dynamic environments

Evolutionary functional testing of continuous control systems

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Theoretical analysis of fitness-proportional selection: landscapes and efficiency

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Gene transposon based clonal selection algorithm for clustering

Robustness of multiple objective GP stock-picking in unstable financial markets: real-world applications track

Orthogonal learning particle swarm optimization

Co-evolvability of games in coevolutionary genetic algorithms

Meta-heuristics for reconstructing cross cut shredded text documents

Evolutionary continuation methods for optimization problems

Analysis of evolutionary algorithms on the one-dimensional spin glass with power-law interactions

Genetic programming in the wild: evolving unrestricted bytecode

Improving Markov chain classification using string transformations and evolutionary search

Multi material topological optimization of structures and mechanisms

Ant colony system based on receding horizon control for aircraft arrival sequencing and scheduling

SRaDE: an adaptive differential evolution based on stochastic ranking

New insights into the OCST problem: integrating node degrees and their location in the graph

Evolutionary multi-objective quantum control experiments with the covariance matrix adaptation

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Optimization of the trading rule in foreign exchange using genetic algorithm

Futility-based offspring sizing

A polynomial time computation of the exact correlation structure of k-satisfiability landscapes

Using a distance metric to guide PSO algorithms for many-objective optimization

Overlapped community detection in complex networks

Evolving stochastic processes using feature tests and genetic programming

Geometrical insights into the dendritic cell algorithm

Evolutionary optimization of multistage interconnection networks performance

Cooperative and decomposable approaches on royal road functions: overcoming the random mutation hill-climber

New heuristic and hybrid genetic algorithm for solving the bounded diameter minimum spanning tree problem

Pareto cooperative coevolutionary genetic algorithm using reference sharing collaboration

Evolution of team composition in multi-agent systems

New entropy model for extraction of structural information from XCS population

A highly configurable test system for evolutionary black-box testing of embedded systems

Limitations of the fitness-proportional negative slope coefficient as a difficulty measure

Partial neighborhoods of elementary landscapes

Improving prediction in evolutionary algorithms for dynamic environments

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Free lunches for neural network search

IMAD: in-execution malware analysis and detection

An improved small-sample statistical test for comparing the success rates of evolutionary algorithms

Prediction in evolutionary algorithms for dynamic environments using markov chains and nonlinear regression

Discovering a domain alphabet

Discrete dynamical genetic programming in XCS

Tracking multiple objects in non-stationary video

Centric selection: a way to tune the exploration/exploitation trade-off

Incorporating expert knowledge in evolutionary search: a study of seeding methods

uQFCS: QFCS with unfixed fuzzy sets in continuous multi-step environments with continuous vector actions

Evolvable malware

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Genetic programming for protein related text classification

On the scalability of XCS(F)

Evolutionary search and convertible agents for the simultaneous type and dimensional synthesis of planar mechanisms

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Graph structured program evolution with automatically defined nodes

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Tunneling between optima: partition crossover for the traveling salesman problem

Operator equalisation, bloat and overfitting: a study on human oral bioavailability prediction

Genetic programming methodology that synthesize vegetation indices for the estimation of soil cover

An evolutionary algorithm with species-specific explosion for multimodal optimization

Genetic programming based image segmentation with applications to biomedical object detection

Mixed heuristic and mathematical programming using reference points for dynamic data types optimization in multimedia embedded systems

Program optimization by random tree sampling

Optimization of dynamic memory managers for embedded systems using grammatical evolution

Using crossover based similarity measure to improve genetic programming generalization ability

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Binary encoding for prototype tree of probabilistic model building GP

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Soft memory for stock market analysis using linear and developmental genetic programming

Behavioural GP diversity for adaptive stock selection

Investigating and exploiting the bias of the weighted hypervolume to articulate user preferences

Content Provider	ACM Digital Library
Author	Bader, Johannes Zitzler, Eckart Auger, Anne Brockhoff, Dimo
Abstract	Optimizing the hypervolume indicator within evolutionary multiobjective optimizers has become popular in the last years. Recently, the indicator has been generalized to the weighted case to incorporate various user preferences into hypervolume-based search algorithms. There are two main open questions in this context: (i) how does the specified weight influence the distribution of a fixed number of points that maximize the weighted hypervolume indicator? (ii) how can the user articulate her preferences easily without specifying a certain weight distribution function? In this paper, we tackle both questions. First, we theoretically investigate optimal distributions of μ points that maximize the weighted hypervolume indicator. Second, based on the obtained theoretical results, we propose a new approach to articulate user preferences within biobjective hypervolume-based optimization in terms of specifying a desired density of points on a predefined (imaginary) Pareto front. Within this approach, a new exact algorithm based on dynamic programming is proposed which selects the set of μ points that maximizes the (weighted) hypervolume indicator. Experiments on various test functions show the usefulness of this new preference articulation approach and the agreement between theory and practice.
Starting Page	563
Ending Page	570
Page Count	8
File Format	PDF
ISBN	9781605583259
DOI	10.1145/1569901.1569980
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2009-07-08
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Preference articulation Hypervolume indicator
Content Type	Text
Resource Type	Article

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