Leiden University Scholarly Publications

Maulana, A. 2018

Many objective optimization and complex network analysis

Doctoral Thesis

open access

This thesis seeks to combine two different research topics; Multi-Objective Optimization and Complex Network Analysis. Multi-Objective Optimization aims at finding a set of optimal, non-dominated... Show moreThis thesis seeks to combine two different research topics; Multi-Objective Optimization and Complex Network Analysis. Multi-Objective Optimization aims at finding a set of optimal, non-dominated solutions for optimization problems with multiple (actually, many) conflicting objectives. There is a wide range of applications of multi-objective optimization such as in science, engineering design, network analysis, chemical processes, delivery of products, economics and logistics, medical health and so forth. Since one often faces problems with a larger number of objective functions to be optimized simultaneously, the research topic has shifted to Many-Objective Optimization, which means optimization with (far) more than three objective functions. Complex network analysis is a research field that deals with analyzing large networks. In this research line, there are some active research topics, such as controlling complex networks, finding communities in a network, and measuring the importance of nodes in networks. Due to the bigger amount of data and more difficult problems arising in complex network analysis, research in this field has increased significantly. To this end, more complex networks has given the challenge of finding better approaches in dealing with the problem to yield some adequate result of an analysis. Show less

Zhao, J.; Basto Fernandes, V.; Jiao, L.; Yevseyeva, I.; Maulana, A.; Li, R.; ... ; Emmerich, M.T.M. 2016

Multiobjective optimization of classifiers by means of 3D convex-hull-based evolutionary algorithms

Article / Letter to editor

open access

The receiver operating characteristic (ROC) and detection error tradeoff(DET) curves are frequently used in the machine learning community to analyze the performance of binary classifiers.... Show moreThe receiver operating characteristic (ROC) and detection error tradeoff(DET) curves are frequently used in the machine learning community to analyze the performance of binary classifiers. Recently, the convex-hull-based multiobjective genetic programming algorithm was proposed and successfully applied to maximize the convex hull area for binary classifi- cation problems by minimizing false positive rate and maximizing true positive rate at the same time using indicator-based evolutionary algorithms. The area under the ROC curve was used for the performance assessment and to guide the search. Here we extend this re- search and propose two major advancements: Firstly we formulate the algorithm in detec- tion error tradeoffspace, minimizing false positives and false negatives, with the advantage that misclassification cost tradeoffcan be assessed directly. Secondly, we add complexity as an objective function, which gives rise to a 3D objective space (as opposed to a 2D pre- vious ROC space). A domain specific performance indicator for 3D Pareto front approxima- tions, the volume above DET surface, is introduced, and used to guide the indicator-based evolutionary algorithm to find optimal approximation sets. We assess the performance of the new algorithm on designed theoretical problems with different geometries of Pareto fronts and DET surfaces, and two application-oriented benchmarks: (1) Designing spam filters with low numbers of false rejects, false accepts, and low computational cost us- ing rule ensembles, and (2) finding sparse neural networks for binary classification of test data from the UCI machine learning benchmark. The results show a high performance of the new algorithm as compared to conventional methods for multicriteria optimization. Show less