Generating coefficients for optimization test problems with implicit correlation induction

Reilly, Charles H.

doi:10.1109/icsmc.1997.635293

Cited by 10 publications

(2 citation statements)

References 18 publications

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“…Studies such as [18] and [19] highlight the importance of understanding correlation between objectives. Indeed, methods developed for many-objective problems often rely on correlation to effectively employ dimension reduction techniques in order to better to solve them.…”

Section: Correlation Functionsmentioning

confidence: 99%

Benchmark multi-objective optimisation test problems with mixed encodings

McClymont

Keedwell

2011

2011 IEEE Congress of Evolutionary Computation (CEC)

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The fields of multi-objective combinatorial and continuous optimisation have both experienced a significant and rapid growth in publications and research since the turn of the century. Despite this it is more often than not that methods in both fields are applied only to problems of one type of encoding. However, many real-world problems require parameters vectors that use multiple encodings. This paper presents a suite of novel multi-objective optimisation test problems (Exeter1 to 6) with mixed encodings (real and binary) and offer variable correlation between the objectives at different stages of the search. The problems are demonstrated using NSGA-II, SPEA2 and a (μ+λ) Evolution Strategy which were modified to operate on both encodings simultaneously.

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Section: Correlation Functionsmentioning

confidence: 99%

Benchmark multi-objective optimisation test problems with mixed encodings

McClymont

Keedwell

2011

2011 IEEE Congress of Evolutionary Computation (CEC)

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“…This paper represents an updating and expansion of Reilly (1997). It covers newer ICI methods for the 0-1 knapsack problem and describes how target population correlations can be approximated for each of the ICI methods considered.…”

Section: Introductionmentioning

confidence: 99%

Synthetic Optimization Problem Generation: Show Us the Correlations!

Reilly

2009

INFORMS Journal on Computing

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I n many computational experiments, correlation is induced between certain types of coefficients in synthetic (or simulated) instances of classical optimization problems. Typically, the correlations that are induced are only qualified-that is, described by their presumed intensity. We quantify the population correlations induced under several strategies for simulating 0-1 knapsack problem instances and also for correlation-induction approaches used to simulate instances of the generalized assignment, capital budgeting (or multidimensional knapsack), and set-covering problems. We discuss implications of these correlation-induction methods for previous and future computational experiments on simulated optimization problems.

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Simulating realistic set covering problems with known optimal solutions

Sapkota

Reilly

2011

Computers & Industrial Engineering

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Generating coefficients for optimization test problems with implicit correlation induction

Cited by 10 publications

References 18 publications

Benchmark multi-objective optimisation test problems with mixed encodings

Benchmark multi-objective optimisation test problems with mixed encodings

Synthetic Optimization Problem Generation: Show Us the Correlations!

Simulating realistic set covering problems with known optimal solutions

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