Lecture Notes in Artificial Intelligence 10142

Subseries of Lecture Notes in Computer Science

LNAI Series Editors

Randy GoebelUniversity of Alberta, Edmonton, Canada

Yuzuru TanakaHokkaido University, Sapporo, Japan

Wolfgang WahlsterDFKI and Saarland University, Saarbrücken, Germany

LNAI Founding Series Editor

Joerg SiekmannDFKI and Saarland University, Saarbrücken, Germany

More information about this series at http://www.springer.com/series/1244

Markus Wagner • Xiaodong LiTim Hendtlass (Eds.)

Artificial Lifeand ComputationalIntelligenceThird Australasian Conference, ACALCI 2017Geelong, VIC, Australia, January 31 – February 2, 2017Proceedings

123

EditorsMarkus WagnerThe University of AdelaideAdelaide, SAAustralia

Xiaodong LiRMIT UniversityMelbourne, VICAustralia

Tim HendtlassSwinburne UniversityMelbourne, VICAustralia

ISSN 0302-9743 ISSN 1611-3349 (electronic)Lecture Notes in Artificial IntelligenceISBN 978-3-319-51690-5 ISBN 978-3-319-51691-2 (eBook)DOI 10.1007/978-3-319-51691-2

Library of Congress Control Number: 2016961325

LNCS Sublibrary: SL7 – Artificial Intelligence

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Preface

This volume contains the papers presented at the Australasian Conference on ArtificialLife and Computational Intelligence (ACALCI 2017) held from January 31 to February2, 2017, in Geelong, Australia.

The research areas of artificial life and computational intelligence have grownsignificantly over recent years. The breadth is reflected in the papers addressing diverseaspects in the domain, from theoretical developments to learning, optimization, andapplications of such methods to real-world problems.

This volume presents 32 papers, many of them authored by leading researchers inthe field. After a rigorous evaluation of all 47 submissions by the international ProgramCommittee, 32 manuscripts were selected for single-track oral presentation at ACALCI2017. All papers underwent a full peer-review with three to four reviewers per paper.

The ACALCI 2017 international Program Committee consisted of over 63 membersfrom six countries, based on their affiliation. We would like to thank the membersof the international Program Committee, the ACALCI Steering Committee, the localOrganizing Committee, and other members of the organization team for their com-mendable efforts and contributions to the conference.

We would like to acknowledge the support from RMIT University, Melbourne, andthe organizers of the Australian Computer Science Week (ACSW), who kindly allowedACALCI 2017 to be co-located with ACSW 2017 at Deakin University, Geelong.

The support and assistance from Springer and EasyChair are gratefullyacknowledged.

November 2016 Markus WagnerXiaodong Li

Tim Hendtlass

Organization

Conference Chairs

General Chair

Xiaodong Li RMIT University, Australia

Program Co-chairs

Markus Wagner University of Adelaide, AustraliaTim Hendtlass Swinburne University, Australia

Paper and Poster Award Committee Chair

Vic Ciesielski RMIT University, Australia

Special Session Chair

Aldeida Aleti Monash University, Australia

Treasurer and Registration Chair

Andy Song RMIT University, Australia

Publicity Chairs

Fabio Zambetta RMIT University, AustraliaKai Qin RMIT University, AustraliaBing Xue Victoria University of Wellington, New Zealand

Webmaster

Wanru Gao University of Adelaide, Australia

Program Committee

Alan Blair University of New South Wales, AustraliaAlan Dorin Monash University, AustraliaAldeida Aleti Monash University, AustraliaAndrea Soltoggio Loughborough University, UKAndreas Ernst Monash University, AustraliaAndrew Lewis Griffith University, Australia

Andy Song RMIT University, AustraliaAneta Neumann University of Adelaide, AustraliaArindam Dey University of South Australia, AustraliaBing Xue Victoria University of Wellington, New ZealandBrad Alexander University of Adelaide, AustraliaBrijesh Verma Central Queensland University, AustraliaDaniel Le Berre CNRS - Université d’Artois, FranceDianhui Wang La Trobe University, AustraliaFabio Zambetta RMIT University, AustraliaFrank Neumann University of Adelaide, AustraliaFrederic Maire Queensland University of Technology, AustraliaHussein Abbass University of New South Wales, AustraliaIckjai Lee James Cook University, AustraliaInaki Rano Intelligent Systems Research Centre, UKIrene Moser Swinburne University of Technology, AustraliaJeff Chan University of Melbourne, AustraliaJianhua Yang Western Sydney University, AustraliaJunbin Gao University of Sydney, AustraliaJunhua Wu University of Adelaide, AustraliaKai Qin RMIT University, AustraliaKevin Korb Monash University, AustraliaLee Altenberg Konrad Lorenz Institute for Evolution and Cognition

Research, AustriaMarc Adam University of Newcastle, AustraliaMarcus Gallagher University of Queensland, AustraliaMarcus Randall Bond University, New ZealandMarkus Wagner University of Adelaide, AustraliaMichael Mayo University of Waikato, New ZealandMohamed Abdelrazek Swinburne University of Technology, AustraliaMohammad Reza Bonyadi University of Adelaide, AustraliaMuhammad Iqbal Victoria University of Wellington, New ZealandNasser Sabar RMIT University, AustraliaNing Gu University of South AustraliaOliver Obst Western Sydney University, AustraliaPablo Moscato University of Newcastle, UKPaul Kwan University of New England, AustraliaPeter Whigham University of Otago, New ZealandRan Cheng University of Surrey, UKRegina Berretta University of Newcastle, UKRobert Burdett Queensland University of Technology, AustraliaStephan Chalup University of Newcastle, UKStephen Chen York University, UKTim Hendtlass Swinburne University, AustraliaTom Cai University of Sydney, AustraliaTommaso Urli CSIRO Data61/NICTA, AustraliaVicky Mak Deakin University, Australia

VIII Organization

Wanru Gao University of Adelaide, AustraliaWilliam Raffe RMIT University, AustraliaWinyu Chinthammit University of Tasmania, AustraliaXiaodong Li RMIT University, AustraliaYi Mei Victoria University of Wellington, New Zealand

Organization IX

Contents

Artificial Life and Computational Intelligence

Extending the Delaunay Triangulation Based Density Measurementto Many-Objective Optimization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

Yutao Qi, Haodong Guo, and Xiaodong Li

Emotion, Trustworthiness and Altruistic Punishmentin a Tragedy of the Commons Social Dilemma. . . . . . . . . . . . . . . . . . . . . . 12

Garrison Greenwood, Hussein A. Abbass, and Eleni Petraki

Equity Option Strategy Discovery and Optimization Using a MemeticAlgorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25

Richard Tymerski, Garrison Greenwood, and Devin Sills

Co-Evolving Line Drawings with Hierarchical Evolution . . . . . . . . . . . . . . . 39Darwin Vickers, Jacob Soderlund, and Alan Blair

Reliability Estimation of Individual Multi-target Regression Predictions . . . . . 50Martin Jakomin and Zoran Bosnić

Feedback Modulated Attention Within a Predictive Framework. . . . . . . . . . . 61Benjamin Cowley and John Thornton

A Batch Infill Strategy for Computationally ExpensiveOptimization Problems. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74

Ahsanul Habib, Hemant Kumar Singh, and Tapabrata Ray

Automatic Clustering and Summarisation of Microblogs:A Multi-subtopic Phrase Reinforcement Algorithm . . . . . . . . . . . . . . . . . . . 86

Mahfouth Alghamdi and Haifeng Shen

Generation and Exploration of Architectural Form Using a CompositeCellular Automata . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99

Camilo Cruz, Michael Kirley, and Justyna Karakiewicz

Wrapper Feature Construction for Figure-Ground Image SegmentationUsing Genetic Programming. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 111

Yuyu Liang, Mengjie Zhang, and Will N. Browne

Surrogate-Assisted Multi-swarm Particle Swarm Optimizationof Morphing Airfoils . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 124

Francesco Fico, Francesco Urbino, Robert Carrese,Pier Marzocca, and Xiaodong Li

Applying Dependency Patterns in Causal Discovery of LatentVariable Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 134

Xuhui Zhang, Kevin B. Korb, Ann E. Nicholson, and Steven Mascaro

An Evolutionary Multi-criteria Journey Planning Algorithm for MultimodalTransportation Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 144

Mohammad Haqqani, Xiaodong Li, and Xinghuo Yu

Estimating Passenger Preferences Using Implicit Relevance Feedbackfor Personalized Journey Planning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 157

Mohammad Haqqani, Xiaodong Li, and Xinghuo Yu

Quantitative Assessment of Heart Function: A Hybrid Mechanismfor Left Ventricle Segmentation from Cine MRI Sequences . . . . . . . . . . . . . 169

Muhammad Sohaib and Jong-Myon Kim

A Hybrid Feature Selection Scheme Based on Local Compactness andGlobal Separability for Improving Roller Bearing Diagnostic Performance . . . 180

M.M. Manjurul Islam, Md. Rashedul Islam, and Jong-Myon Kim

Reliable Fault Diagnosis of Bearings Using Distance and Density Similarityon an Enhanced k-NN . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 193

Dileep Kumar Appana, Md. Rashedul Islam, and Jong-Myon Kim

Towards Solving TSPN with Arbitrary Neighborhoods:A Hybrid Solution. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 204

Bo Yuan and Tiantian Zhang

Detectable Genetic Algorithms-Based Techniques for Solving DynamicOptimisation Problem with Unknown Active Variables . . . . . . . . . . . . . . . . 216

AbdelMonaem F.M. AbdAllah, Daryl L. Essam, and Ruhul A. Sarker

Neighbourhood Analysis: A Case Study on Google MachineReassignment Problem. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 228

Ayad Turky, Nasser R. Sabar, and Andy Song

Optimisation Algorithms and Applications

Multi-objective Optimisation with Multiple Preferred Regions . . . . . . . . . . . 241Md. Shahriar Mahbub, Markus Wagner, and Luigi Crema

An Adaptive Memetic Algorithm for the ArchitectureOptimisation Problem . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 254

Nasser R. Sabar and Aldeida Aleti

Resource Constrained Job Scheduling with Parallel Constraint-Based ACO. . . . 266Dror Cohen, Antonio Gómez-Iglesias, Dhananjay Thiruvady,and Andreas T. Ernst

XII Contents

An Iterated Local Search with Guided Perturbation for the HeterogeneousFleet Vehicle Routing Problem with Time Windowsand Three-Dimensional Loading Constraints . . . . . . . . . . . . . . . . . . . . . . . . 279

Ayad Turky, I. Moser, and Aldeida Aleti

A Memetic Cooperative Co-evolution Model for Large Scale ContinuousOptimization. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 291

Yuan Sun, Michael Kirley, and Saman K. Halgamuge

Investigating the Generality of Genetic Programming BasedHyper-heuristic Approach to Dynamic Job Shop Schedulingwith Machine Breakdown. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 301

John Park, Yi Mei, Su Nguyen, Gang Chen, and Mengjie Zhang

Exploratory Analysis of Clustering Problems Using a Comparisonof Particle Swarm Optimization and Differential Evolution . . . . . . . . . . . . . . 314

Sobia Saleem and Marcus Gallagher

A PSO-Based Reference Point Adaption Method for Genetic ProgrammingHyper-Heuristic in Many-Objective Job Shop Scheduling. . . . . . . . . . . . . . . 326

Atiya Masood, Yi Mei, Gang Chen, and Mengjie Zhang

Optimal Power Allocation of Wireless Sensor Networkswith Multi-operator Based Constrained Differential Evolution. . . . . . . . . . . . 339

Yanan Li, Wenyin Gong, and Zhihua Cai

CEMAB: A Cross-Entropy-based Method for Large-ScaleMulti-Armed Bandits . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 353

Erli Wang, Hanna Kurniawati, and Dirk P. Kroese

Binary PSO for Web Service Location-Allocation . . . . . . . . . . . . . . . . . . . . 366Boxiong Tan, Hai Huang, Hui Ma, and Mengjie Zhang

A MOEA/D with Non-uniform Weight Vector Distribution Strategyfor Solving the Unit Commitment Problem in Uncertain Environment . . . . . . 378

Anupam Trivedi, Dipti Srinivasan, Kunal Pal, and Thomas Reindl

Author Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 391

Contents XIII