18 Result(s)
within
English
Computer Science
Algorithm Analysis and Problem Complexity
Math Applications in Computer Science
**n Hua Zhou
Chapter
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Chapter
Subset Selection
This chapter studies the general subset selection problem that is involved in various learning problems. Based on Pareto optimization, we present the POSS algorithm that achieves equal or better performance th...
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Preliminaries
This chapter introduces preliminaries. Including basic evolutionary algorithms, pseudo-Boolean functions for theoretical studies, and basic knowledge for analyzing running time complexity of evolutionary algor...
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Subset Selection: Ratio Minimization
This chapter studies minimizing the ratio \( f/g \) , such as optimizing the F-measure objective in machine learning tasks. We p...
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Running Time Analysis: Switch Analysis
This chapter presents the switch analysis approach for analyzing the running time complexity of evolutionary algorithms. Switch analysis works by comparing two optimization processes, thus can help analyze a c...
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Subset Selection: Acceleration
This chapter presents the parallel version of Pareto optimization algorithm, PPOSS, for subset selection. We disclose that the parallelization does not break the effectiveness of Pareto optimization while redu...
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Approximation Analysis: SEIP
This chapter studies the approximation performance of evolutionary algorithms through the SEIP framework. SEIP adopts an isolation function to manage competition among solutions and offers a general characteri...
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Population
This chapter studies the influence of population on evolutionary algorithms. We show that, on one hand, population is unexpected for simple functions such as OneMax and LeadningOnes by derving the lower runnin...
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Introduction
This chapter briefly introduces basic concepts including machine learning, evolutionary learning, multi-objective optimization, as well as the organization of the book.
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Running Time Analysis: Convergence-based Analysis
This chapter presents the convergence-based analysis approach for analyzing the running time complexity of evolutionary algorithms, which is derived from bridging two fundamental theoretical issues. The approa...
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Subset Selection: Noise
This chapter studies the subset selection problem under multiplicative and additive noise. We disclose that the greedy algorithm and POSS algorithms achieve nearly the same approximation guarantee under noise....
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Running Time Analysis: Comparison and Unification
This chapter studies the relationship among different analysis approaches for running time complexity of evolutionary algorithms, through the defined reducibility relation between two approaches. Consequently,...
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Boundary Problems of EAs
This chapter studies the easiest and hardest instances of a problem class respect to the given evolutionary algorithm, for the understanding of the algorithm. Through the derived theorem, the easiest and harde...
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Inaccurate Fitness Evaluation
This chapter studies the influence of noise on evolutionary algorithms. We disclose that the noise is not always bad. For hard problems, noise can be helpful, while for easy problems, it can be harmful. The fi...
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Constrained Optimization
This chapter studies how to deal with infeasible solutions when evolutionary algorithms are used for constrained optimization. We derive sufficient and necessary conditions to judge the usefulness of infeasibl...
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Recombination
This chapter studies the influence of recombination operators. We show that, in multi-objective evolutionary optimization, recombination operators are useful for multi-objective evolutionary optimization by ac...
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Selective Ensemble
This chapter studies the evolutionary learning method for selective ensemble learning problem, which needs to select some component learners out of all learners. We show that a Pareto optimization algorithm, P...
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Subset Selection: k-Submodular Maximization
This chapter studies an extension of the subset selection problem, i.e., maximizing monotone k-submodular functions subject to a size constraint. Based on Pareto optimization, we present the POkSS algorithm for t...
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Chapter
Representation
This chapter studies the influence of solution representation, by comparing the genetic programming with the genetic algorithm, which employ tree representation and vector representation, respectively. We show...
