15 Result(s)
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Chapter and Conference Paper
Approximating Pareto Fronts in Evolutionary Multiobjective Optimization with Large Population Size
Approximating the Pareto fronts (PFs) of multiobjective optimization problems (MOPs) with a population of nondominated solutions is a common strategy in evolutionary multiobjective optimization (EMO). In the c...
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Chapter and Conference Paper
A Clustering-Based Multiobjective Evolutionary Algorithm for Balancing Exploration and Exploitation
This paper proposes a simple but promising clustering-based multi-objective evolutionary algorithm, termed as CMOEA. At each generation, CMOEA first divides the current population into several subpopulations b...
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Chapter and Conference Paper
Adjustment of Weight Vectors of Penalty-Based Boundary Intersection Method in MOEA/D
Multi-objective Evolutionary Algorithm Based on Decomposition (MOEA/D) is one of the dominant algorithmic frameworks for multi-objective optimization in the area of evolutionary computation. The performance of...
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Chapter and Conference Paper
Multi-objective Techniques for Single-Objective Local Search: A Case Study on Traveling Salesman Problem
In this paper, we show that the techniques widely used in multi-objective optimization can help a single-objective local search procedure escape from local optima and find better solutions. The Traveling Sales...
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Article
MOEA/D with chain-based random local search for sparse optimization
The goal in sparse approximation is to find a sparse representation of a system. This can be done by minimizing a data-fitting term and a sparsity term at the same time. This sparse term imposes penalty for sp...
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Chapter and Conference Paper
A Hybrid Discrete Artificial Bee Colony Algorithm for Multi-objective Blocking Lot-Streaming Flow Shop Scheduling Problem
A blocking lot-streaming flow shop (BLSFS) scheduling problem involves in splitting a job into several sublots and no capacity buffers with blocking between adjacent machines. It is of popularity in real-world...
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Chapter and Conference Paper
A New Steady-State MOEA/D for Sparse Optimization
The classical algorithms based on regularization usually solve sparse optimization problems under the framework of single objective optimization, which combines the sparse term with the loss term. The majority...
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Chapter and Conference Paper
A Reference-Inspired Evolutionary Algorithm with Subregion Decomposition for Many-Objective Optimization
In this paper, we propose a reference-inspired multiobjective evolutionary algorithm for many-objective optimisation. The main idea is (1) to summarise information inspired by a set of randomly generated refer...
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Article
Combinations of estimation of distribution algorithms and other techniques
This paper summaries our recent work on combining estimation of distribution algorithms (EDA) and other techniques for solving hard search and optimization problems: a) guided mutation, an offspring generator ...
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Chapter and Conference Paper
Robust Visual Mining of Data with Error Information
Recent results on robust density-based clustering have indicated that the uncertainty associated with the actual measurements can be exploited to locate objects that are atypical for a reason unrelated to meas...
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Chapter
Estimation of Distribution Algorithm with 2-opt Local Search for the Quadratic Assignment Problem
This chapter proposes a combination of estimation of distribution algorithm (EDA) and the 2-opt local search algorithm (EDA/LS) for the quadratic assignment problem (QAP). In EDA/LS, a new operator, called guided...
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Chapter and Conference Paper
Adaptive Online Multi-stroke Sketch Recognition Based on Hidden Markov Model
This paper presents a novel approach for adaptive online multi-stroke sketch recognition based on Hidden Markov Model (HMM). The method views the drawing sketch as the result of a stochastic process that is go...
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Chapter and Conference Paper
A Hybrid Estimation of Distribution Algorithm for CDMA Cellular System Design
While code division multiple access (CDMA) is becoming a promising cellular communication system, the design for a CDMA cellular system configuration has posed a practical challenge in optimisation. The study ...
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Chapter and Conference Paper
On Class Visualisation for High Dimensional Data: Exploring Scientific Data Sets
Parametric Embedding (PE) has recently been proposed as a general-purpose algorithm for class visualisation. It takes class posteriors produced by a mixture-based clustering algorithm and projects them in 2D f...
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Chapter and Conference Paper
User Adaptation for Online Sketchy Shape Recognition
This paper presents a method of online sketchy shape recognition that can adapt to different user sketching styles. The adaptation principle is based on incremental active learning and dynamic user modeling. I...
