Multiobjective Optimization in Bioinformatics and Computational Biology

Handl, Julia; Kell, Douglas B.; Knowles, Joshua

doi:10.1109/tcbb.2007.070203

Cited by 271 publications

(143 citation statements)

References 123 publications

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“…Pareto optimization overcomes these limitations by exploring different trade-off solutions between a set of objectives [29], without prior knowledge of a preference vector. Concretely, Pareto optimization employs the principle of Pareto dominance: A solution dominates another solution if it is strictly 3 better in at least one objective and not worse in any objective [14,35].…”

Section: Robust Optimizationmentioning

confidence: 99%

Evolutionary robust optimization in production planning – interactions between number of objectives, sample size and choice of robustness measure

Diaz

Handl

2017

Computers & Operations Research

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We aim to find robust solutions in optimization settings where there is uncertainty associated with the operating/environmental conditions, and the fitness of a solution is hence best described by a distribution of outcomes. In such settings, the nature of the fitness distribution (reflecting the performance of a particular solution across a set of operating scenarios) is of potential interest in deciding solution quality, and previous work has suggested the inclusion of robustness as an additional optimization objective. However, there has been limited investigation of different robustness criteria, and the impact this choice may have on the sample size needed to obtain reliable fitness estimates. Here, we investigate different single and multi-objective formulations for robust optimization, in the context of a real-world problem addressed via simulation-based optimization. For the (limited evaluation) setting considered, our results highlight the value of an explicit robustness criterion in steering an optimizer towards solutions that are not only robust (as may be expected), but also associated with a profit that is, on average, higher than that identified by standard single-objective approaches. We also observe significant interactions between the choice of robustness measure and the sample size employed during fitness evaluation, an effect that is more pronounced for our multi-objective models.

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Section: Robust Optimizationmentioning

confidence: 99%

Evolutionary robust optimization in production planning – interactions between number of objectives, sample size and choice of robustness measure

Diaz

Handl

2017

Computers & Operations Research

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“…, m is a set of m different objective functions (quality index), i.e. that clustering C * corresponds to a cluster solution that has the best optimized m criteria P [27,19].…”

Section: Multi-objective Clusteringmentioning

confidence: 99%

“…These approaches are known as Single-Objective Clustering (SOC) [19]. However, they have some problems: one index is rarely equally well applicable to different types of dataset, i.e.…”

Section: Introductionmentioning

confidence: 99%

A Bi-Objective Clustering Algorithm for Gene Expression Data

Parraga-Alava¹,

Inostroza-Ponta²

2017

CLEIej

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Clustering algorithms are a common method for data analysis in many science field. They have become popular among biologists because of ease to discovery similar cellular functions in gene expression data. Most approaches consider the gene clustering as an optimization problem, where an ad-hoc cluster quality index is optimized which can be defined regarding gene expression data or biological information. However, these approaches may not be sufficient since they cannot guarantee to generate clusters with similar expression patterns and biological coherence. In this paper, we propose a bi-objective clustering algorithm to discover clusters of genes with high levels of co-expression and biological coherence. Our approach uses a multi-objective evolutionary algorithm (MOEA) that optimizes two index based on gene expression level and biological functional classes. The algorithm is tested on three real-life gene expression datasets. Results show that the proposed model yields gene clusters with higher levels of co-expression and biological coherence than traditional approaches.

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“…For example, in protein sequence alignment, maximizing the number of matching bases and minimizing the number of gaps may be two conflicting objectives. Conflicting objectives must also be taken into account in constructing phylogenetic trees and gene regulatory networks [15].…”

Section: A Trade-offs In Biological Systemsmentioning

confidence: 99%

Pareto analysis of evolutionary and learning systems

Jin

Gruna

Sendhoff³

2009

Front. Comput. Sci. China

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Abstract-This paper attempts to argue that most adaptive systems, such as evolutionary or learning systems, have inherently multiple objectives to deal with. Very often, there is no single solution that can optimize all the objectives. In this case, the concept of Pareto optimality is key to analyzing these systems.To support this argument, we first present an example that considers the robustness and evolvability trade-off in a redundant genetic representation for simulated evolution. It is well known that robustness is critical for biological evolution, since without a sufficient degree of mutational robustness, it is impossible for evolution to create new functionalities. On the other hand, the genetic representation should also provide the chance to find new phenotypes, i.e., the ability to innovate. This example shows quantitatively that a trade-off between robustness and innovation does exist in the studied redundant representation.Interesting results will also be given to show that new insights into learning problems can be gained when the concept of Pareto optimality is applied to machine learning. In the first example, a Pareto-based multi-objective approach is employed to alleviate catastrophic forgetting in neural network learning. We show that learning new information and memorizing learned knowledge are two conflicting objectives, and a major part of both information can be memorized when the multiobjective learning approach is adopted. In the second example, we demonstrate that a Pareto-based approach can address neural network regularization more elegantly. By analyzing the Pareto-optimal solutions, it is possible to identifying interesting solutions on the Pareto front.

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Multiobjective Optimization in Bioinformatics and Computational Biology

Cited by 271 publications

References 123 publications

Evolutionary robust optimization in production planning – interactions between number of objectives, sample size and choice of robustness measure

Evolutionary robust optimization in production planning – interactions between number of objectives, sample size and choice of robustness measure

A Bi-Objective Clustering Algorithm for Gene Expression Data

Pareto analysis of evolutionary and learning systems

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