Skip to main content

Mustafa Misir

Assoc.Professor of Computational Science at Duke Kunshan University
DKU Faculty

Selected Publications


Q-Learning Based Framework for Solving the Stochastic E-waste Collection Problem

Chapter · January 1, 2024 Electrical and Electronic Equipment (EEE) has evolved into a gateway for accessing technological innovations. However, EEE imposes substantial pressure on the environment due to the shortened life cycles. E-waste encompasses discarded EEE and its component ... Full text Cite

Algorithm selection for protein-ligand docking: strategies and analysis on ACE.

Journal Article Scientific reports · May 2023 The present study investigates the use of algorithm selection for automatically choosing an algorithm for any given protein-ligand docking task. In drug discovery and design process, conceptualizing protein-ligand binding is a major problem. Targeting this ... Full text Cite

Algorithm Selection for Large-Scale Multi-objective Optimization

Chapter · January 1, 2023 The present study applies Algorithm Selection to automatically specify the suitable algorithms for Large-Scale Multi-objective Optimization. Algorithm Selection has known to benefit from the strengths on multiple algorithm rather than relying one. This tra ... Full text Cite

Neural Network Based Heuristic Selection for Selection Hyper-Heuristics

Conference 2023 IEEE Congress on Evolutionary Computation, CEC 2023 · January 1, 2023 The present study utilizes neural network to perform heuristic selection in selection hyper-heuristics. Selection hyper-heuristics are problem-independent solvers, preferably benefited for tackling a wide range of search and optimization problems. Unlike t ... Full text Cite

An Adaptive Large Neighborhood Search for Heterogeneous Vehicle Routing Problem with Time Windows

Conference IEEE International Conference on Automation Science and Engineering · January 1, 2023 The heterogeneous vehicle routing problem with time windows (HVRPTW) employs various vehicles with different capacities to serve upcoming pickup and delivery orders. We introduce a HVRPTW variant for reflecting the practical needs of crowd-shipping by cons ... Full text Cite

Characterization of CEC Single-Objective Optimization Competition Benchmarks and Algorithms

Conference 2023 IEEE Symposium Series on Computational Intelligence, SSCI 2023 · January 1, 2023 The present study provides an analysis on the characteristics of single-objective optimization benchmark problems as well as the algorithms used to solve them. The target optimization domain involves the CEC competitions, each consisting a set of mathemati ... Full text Cite

Algorithm Selection for the Team Orienteering Problem

Conference Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) · January 1, 2022 This work utilizes Algorithm Selection for solving the Team Orienteering Problem (TOP). The TOP is an NP-hard combinatorial optimization problem in the routing domain. This problem has been modelled with various extensions to address different real-world p ... Full text Cite

Automated Portfolio Generation for Selection Hyper-heuristics: an Application to Protein Structure Prediction on 2D HP Model

Conference 2022 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2022 · January 1, 2022 The present study introduces two methods for constructing portfolios of low-level heuristics, to be used by Selection Hyper-heuristics to solve Protein Structure Prediction with the 2D HP model. Protein Structure Prediction is useful and practical for vari ... Full text Cite

Cross-domain Algorithm Selection: Algorithm Selection across Selection Hyper-heuristics

Conference Proceedings of the 2022 IEEE Symposium Series on Computational Intelligence, SSCI 2022 · January 1, 2022 The present study introduces algorithm selection on selection hyper-heuristics. Hyper-heuristics are known as problem-independent methods utilized to solve different instances from varying problem domains. In the literature, there has been effective hyper- ... Full text Cite

Algorithm Selection across Algorithm Configurators A Case Study on Multi-objective Optimization

Conference Proceedings of the 2022 IEEE Symposium Series on Computational Intelligence, SSCI 2022 · January 1, 2022 The present work utilizes Algorithm Selection for automatically specifying the parameter tuning method for a given tuning task. The idea of parameter tuning is motivated by the premature algorithm designs and their sub-optimal or poor parameter value choic ... Full text Cite

A Bi-Objective Constrained Robust Gate Assignment Problem: Formulation, Instances and Algorithm.

Journal Article IEEE transactions on cybernetics · September 2021 The gate assignment problem (GAP) aims at assigning gates to aircraft considering operational efficiency of airport and satisfaction of passengers. Unlike the existing works, we model the GAP as a bi-objective constrained optimization problem. The total wa ... Full text Cite

Hyper-heuristics: Autonomous Problem Solvers

Chapter · January 1, 2021 Algorithm design is a general task for any problem-solving scenario. For Search and Optimization, this task becomes rather challenging due to the immense algorithm design space. Those existing design options are usually traversed to devise algorithms by th ... Full text Cite

Benchmark Set Reduction for Cheap Empirical Algorithmic Studies

Conference 2021 IEEE Congress on Evolutionary Computation, CEC 2021 - Proceedings · January 1, 2021 The present paper introduces a benchmark set reduction strategy that can degrade the experimental evaluation cost for the algorithmic studies. Algorithm design is an iterative development process within in a test-revise loop. Starting a devised algorithm's ... Full text Cite

Algorithm Selection on Adaptive Operator Selection: A Case Study on Genetic Algorithms

Conference Learning and Intelligent Optimization · 2021 The present study applies Algorithm Selection (AS) to Adaptive Operator Selection (AOS) for further improving the performance of the AOS methods. AOS aims at delivering high performance in solving a given problem through combining the strengths of multiple ... Cite

Generalized Automated Energy Function Selection for Protein Structure Prediction on 2D and 3D HP Models

Conference 2021 IEEE Symposium Series on Computational Intelligence, SSCI 2021 - Proceedings · January 1, 2021 The present work applies Algorithm Selection for automatically determining the best energy functions for search algorithms on Protein Structure Prediction. Protein Structure Prediction is a critical problem concerned with exploring the structure of a prote ... Full text Cite

Selection-based Per-Instance Heuristic Generation for Protein Structure Prediction of 2D HP Model

Conference 2021 IEEE Symposium Series on Computational Intelligence, SSCI 2021 - Proceedings · January 1, 2021 The present study aims at generating heuristics for Protein Structure Prediction represented in the 2D HP model. Protein Structure Prediction is about determining the 3-dimensional form of a protein from a given amino acid sequence. The resulting structure ... Full text Cite

Active Matrix Completion for Algorithm Selection

Conference Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) · January 1, 2019 The present work accommodates active matrix completion to generate cheap and informative incomplete algorithm selection datasets. Algorithm selection is being used to detect the best possible algorithm(s) for a given problem ((formula presented) instance). ... Full text Cite

Towards Personalized Data-Driven Bundle Design with QoS Constraint

Chapter · January 1, 2019 In this paper, we study the bundle design problem for offering personalized bundles of services using historical consumer redemption data. The problem studied here is for an operator managing multiple service providers, each responsible for an attraction, ... Full text Cite

A case study of algorithm selection for the traveling thief problem

Journal Article Journal of Heuristics · June 1, 2018 Many real-world problems are composed of several interacting components. In order to facilitate research on such interactions, the Traveling Thief Problem (TTP) was created in 2013 as the combination of two well-understood combinatorial optimization proble ... Full text Cite

Data sampling through collaborative filtering for algorithm selection

Conference 2017 IEEE Congress on Evolutionary Computation, CEC 2017 - Proceedings · July 5, 2017 Algorithm selection has been studied to specify the best possible algorithm(s) for a given problem instance. One of the major drawbacks of the algorithm selection methods is their need for the performance data. The performance data involves the performance ... Full text Cite

Q-Learning Based Framework for Solving the Stochastic E-waste Collection Problem

Chapter · January 1, 2024 Electrical and Electronic Equipment (EEE) has evolved into a gateway for accessing technological innovations. However, EEE imposes substantial pressure on the environment due to the shortened life cycles. E-waste encompasses discarded EEE and its component ... Full text Cite

Algorithm selection for protein-ligand docking: strategies and analysis on ACE.

Journal Article Scientific reports · May 2023 The present study investigates the use of algorithm selection for automatically choosing an algorithm for any given protein-ligand docking task. In drug discovery and design process, conceptualizing protein-ligand binding is a major problem. Targeting this ... Full text Cite

Algorithm Selection for Large-Scale Multi-objective Optimization

Chapter · January 1, 2023 The present study applies Algorithm Selection to automatically specify the suitable algorithms for Large-Scale Multi-objective Optimization. Algorithm Selection has known to benefit from the strengths on multiple algorithm rather than relying one. This tra ... Full text Cite

Neural Network Based Heuristic Selection for Selection Hyper-Heuristics

Conference 2023 IEEE Congress on Evolutionary Computation, CEC 2023 · January 1, 2023 The present study utilizes neural network to perform heuristic selection in selection hyper-heuristics. Selection hyper-heuristics are problem-independent solvers, preferably benefited for tackling a wide range of search and optimization problems. Unlike t ... Full text Cite

An Adaptive Large Neighborhood Search for Heterogeneous Vehicle Routing Problem with Time Windows

Conference IEEE International Conference on Automation Science and Engineering · January 1, 2023 The heterogeneous vehicle routing problem with time windows (HVRPTW) employs various vehicles with different capacities to serve upcoming pickup and delivery orders. We introduce a HVRPTW variant for reflecting the practical needs of crowd-shipping by cons ... Full text Cite

Characterization of CEC Single-Objective Optimization Competition Benchmarks and Algorithms

Conference 2023 IEEE Symposium Series on Computational Intelligence, SSCI 2023 · January 1, 2023 The present study provides an analysis on the characteristics of single-objective optimization benchmark problems as well as the algorithms used to solve them. The target optimization domain involves the CEC competitions, each consisting a set of mathemati ... Full text Cite

Algorithm Selection for the Team Orienteering Problem

Conference Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) · January 1, 2022 This work utilizes Algorithm Selection for solving the Team Orienteering Problem (TOP). The TOP is an NP-hard combinatorial optimization problem in the routing domain. This problem has been modelled with various extensions to address different real-world p ... Full text Cite

Automated Portfolio Generation for Selection Hyper-heuristics: an Application to Protein Structure Prediction on 2D HP Model

Conference 2022 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2022 · January 1, 2022 The present study introduces two methods for constructing portfolios of low-level heuristics, to be used by Selection Hyper-heuristics to solve Protein Structure Prediction with the 2D HP model. Protein Structure Prediction is useful and practical for vari ... Full text Cite

Cross-domain Algorithm Selection: Algorithm Selection across Selection Hyper-heuristics

Conference Proceedings of the 2022 IEEE Symposium Series on Computational Intelligence, SSCI 2022 · January 1, 2022 The present study introduces algorithm selection on selection hyper-heuristics. Hyper-heuristics are known as problem-independent methods utilized to solve different instances from varying problem domains. In the literature, there has been effective hyper- ... Full text Cite

Algorithm Selection across Algorithm Configurators A Case Study on Multi-objective Optimization

Conference Proceedings of the 2022 IEEE Symposium Series on Computational Intelligence, SSCI 2022 · January 1, 2022 The present work utilizes Algorithm Selection for automatically specifying the parameter tuning method for a given tuning task. The idea of parameter tuning is motivated by the premature algorithm designs and their sub-optimal or poor parameter value choic ... Full text Cite

A Bi-Objective Constrained Robust Gate Assignment Problem: Formulation, Instances and Algorithm.

Journal Article IEEE transactions on cybernetics · September 2021 The gate assignment problem (GAP) aims at assigning gates to aircraft considering operational efficiency of airport and satisfaction of passengers. Unlike the existing works, we model the GAP as a bi-objective constrained optimization problem. The total wa ... Full text Cite

Hyper-heuristics: Autonomous Problem Solvers

Chapter · January 1, 2021 Algorithm design is a general task for any problem-solving scenario. For Search and Optimization, this task becomes rather challenging due to the immense algorithm design space. Those existing design options are usually traversed to devise algorithms by th ... Full text Cite

Benchmark Set Reduction for Cheap Empirical Algorithmic Studies

Conference 2021 IEEE Congress on Evolutionary Computation, CEC 2021 - Proceedings · January 1, 2021 The present paper introduces a benchmark set reduction strategy that can degrade the experimental evaluation cost for the algorithmic studies. Algorithm design is an iterative development process within in a test-revise loop. Starting a devised algorithm's ... Full text Cite

Algorithm Selection on Adaptive Operator Selection: A Case Study on Genetic Algorithms

Conference Learning and Intelligent Optimization · 2021 The present study applies Algorithm Selection (AS) to Adaptive Operator Selection (AOS) for further improving the performance of the AOS methods. AOS aims at delivering high performance in solving a given problem through combining the strengths of multiple ... Cite

Generalized Automated Energy Function Selection for Protein Structure Prediction on 2D and 3D HP Models

Conference 2021 IEEE Symposium Series on Computational Intelligence, SSCI 2021 - Proceedings · January 1, 2021 The present work applies Algorithm Selection for automatically determining the best energy functions for search algorithms on Protein Structure Prediction. Protein Structure Prediction is a critical problem concerned with exploring the structure of a prote ... Full text Cite

Selection-based Per-Instance Heuristic Generation for Protein Structure Prediction of 2D HP Model

Conference 2021 IEEE Symposium Series on Computational Intelligence, SSCI 2021 - Proceedings · January 1, 2021 The present study aims at generating heuristics for Protein Structure Prediction represented in the 2D HP model. Protein Structure Prediction is about determining the 3-dimensional form of a protein from a given amino acid sequence. The resulting structure ... Full text Cite

Active Matrix Completion for Algorithm Selection

Conference Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) · January 1, 2019 The present work accommodates active matrix completion to generate cheap and informative incomplete algorithm selection datasets. Algorithm selection is being used to detect the best possible algorithm(s) for a given problem ((formula presented) instance). ... Full text Cite

Towards Personalized Data-Driven Bundle Design with QoS Constraint

Chapter · January 1, 2019 In this paper, we study the bundle design problem for offering personalized bundles of services using historical consumer redemption data. The problem studied here is for an operator managing multiple service providers, each responsible for an attraction, ... Full text Cite

A case study of algorithm selection for the traveling thief problem

Journal Article Journal of Heuristics · June 1, 2018 Many real-world problems are composed of several interacting components. In order to facilitate research on such interactions, the Traveling Thief Problem (TTP) was created in 2013 as the combination of two well-understood combinatorial optimization proble ... Full text Cite

Data sampling through collaborative filtering for algorithm selection

Conference 2017 IEEE Congress on Evolutionary Computation, CEC 2017 - Proceedings · July 5, 2017 Algorithm selection has been studied to specify the best possible algorithm(s) for a given problem instance. One of the major drawbacks of the algorithm selection methods is their need for the performance data. The performance data involves the performance ... Full text Cite

ALORS: An algorithm recommender system

Journal Article Artificial Intelligence · March 1, 2017 Algorithm selection (AS), selecting the algorithm best suited for a particular problem instance, is acknowledged to be a key issue to make the best out of algorithm portfolios. This paper presents a collaborative filtering approach to AS. Collaborative fil ... Full text Cite

Greedy based pareto local search for bi-objective robust airport gate assignment problem

Conference Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) · January 1, 2017 The present paper proposes a Greedy based Pareto Local Search (GB-PLS) algorithm for the bi-objective robust airport gate assignment problem (bRAGAP). The bRAGAP requires to minimize the total passenger walking distance and the total robust cost of gate as ... Full text Cite

Simulated annealing with a time-slot heuristic for ready-mix concrete delivery

Conference Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) · January 1, 2017 The concrete delivery problem (CDP) is an NP-hard, real world combinatorial optimization problem. The CDP involves tightly interrelated routing and scheduling constraints that have to be satisfied by considering the tradeoff between production and distribu ... Full text Cite

Matrix factorization based benchmark set analysis: A case study on HyFlex

Conference Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) · January 1, 2017 The present paper offers an analysis strategy to examine benchmark sets of combinatorial search problems. Experimental analysis has been widely used to compare a set of algorithms on a group of instances from such problem domains. These studies mostly focu ... Full text Cite

Designing and comparing multiple portfolios of parameter configurations for online algorithm selection

Conference Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) · January 1, 2016 Algorithm portfolios seek to determine an effective set of algorithms that can be used within an algorithm selection framework to solve problems. A limited number of these portfolio studies focus on generating different versions of a target algorithm using ... Full text Cite

Ensemble move acceptance in selection hyper-heuristics

Conference Communications in Computer and Information Science · January 1, 2016 Selection hyper-heuristics are high level search methodologies which control a set of low level heuristics while solving a given problem. Move acceptance is a crucial component of selection hyperheuristics, deciding whether to accept or reject a new soluti ... Full text Cite

An analysis of generalised heuristics for vehicle routing and personnel rostering problems

Journal Article Journal of the Operational Research Society · May 20, 2015 The present study investigates the performance of heuristics while solving problems with routing and rostering characteristics. The target problems include scheduling and routing home care, security and maintenance personnel. In analysing the behaviour of ... Full text Cite

Designing a portfolio of parameter configurations for online algorithm selection

Conference AAAI Workshop - Technical Report · January 1, 2015 Algorithm portfolios seek to determine an effective set of algorithms that can be used within an algorithm selection framework to solve problems. A limited number of these portfolio studies focus on generating different versions of a target algorithm using ... Cite

ADVISER: A web-based algorithm portfolio deviser

Conference Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) · January 1, 2015 Full text Cite

OSCAR: Online selection of algorithm portfolios with case study on memetic algorithms

Conference Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) · January 1, 2015 This paper introduces an automated approach called OSCAR that combines algorithm portfolios and online algorithm selection. The goal of algorithm portfolios is to construct a subset of algorithms with diverse problem solving capabilities. The portfolio is ... Full text Cite

Diversity-Oriented Bi-Objective Hyper-heuristics for Patrol Scheduling

Conference PATAT 2014 - Proceedings of the 10th International Conference on the Practice and Theory of Automated Timetabling · January 1, 2014 The patrol scheduling problem is concerned with assigning security teams to different stations for distinct time intervals while respecting a limited number of contractual constraints. The objective is to minimise the total distance travelled while maximis ... Cite

A recommender system for process discovery

Conference Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) · January 1, 2014 Over the last decade, several algorithms for process discovery and process conformance have been proposed. Still, it is well-accepted that there is no dominant algorithm in any of these two disciplines, and then it is often difficult to apply them successf ... Full text Cite

Building algorithm portfolios for memetic algorithms

Conference GECCO 2014 - Companion Publication of the 2014 Genetic and Evolutionary Computation Conference · January 1, 2014 The present study introduces an automated mechanism to build algorithm portfolios for memetic algorithms. The objective is to determine an algorithm set involving combinations of crossover, mutation and local search operators based on their past performanc ... Full text Cite

Group decision making hyper-heuristics for function optimisation

Conference 2013 13th UK Workshop on Computational Intelligence, UKCI 2013 · December 31, 2013 A hyper-heuristic is a high level methodology which performs search over the space of heuristics each operating on the space of solutions to solve hard computational problems. This search process is based on either generation or selection of low level heur ... Full text Cite

An investigation on the generality level of selection hyper-heuristics under different empirical conditions

Journal Article Applied Soft Computing Journal · January 1, 2013 The present study concentrates on the generality of selection hyper-heuristics across various problem domains with a focus on different heuristic sets in addition to distinct experimental limits. While most hyper-heuristic research employs the term general ... Full text Cite

A new hyper-heuristic as a general problem solver: An implementation in HyFlex

Journal Article Journal of Scheduling · January 1, 2013 This study provides a new hyper-heuristic design using a learning-based heuristic selection mechanism together with an adaptive move acceptance criterion The selection process was supported by an online heuristic subset selection strategy In addition, a pa ... Full text Cite

An intelligent hyper-heuristic framework for CHeSC 2011

Conference Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) · October 30, 2012 The present study proposes a new selection hyper-heuristic providing several adaptive features to cope with the requirements of managing different heuristic sets. The approach suggested provides an intelligent way of selecting heuristics, determines effect ... Full text Cite

The effect of the set of low-level heuristics on the performance of selection hyper-heuristics

Conference Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) · September 24, 2012 The present study investigates the effect of heuristic sets on the performance of several selection hyper-heuristics. The performance of selection hyper-heuristics is strongly dependant on low-level heuristic sets employed for solving target problems. Ther ... Full text Cite

One hyper-heuristic approach to two timetabling problems in health care

Journal Article Journal of Heuristics · June 1, 2012 We present one general high-level hyper-heuristic approach for addressing two timetabling problems in the health care domain: the patient admission scheduling problem and the nurse rostering problem. The complex combinatorial problem of patient admission s ... Full text Cite

Monte Carlo hyper-heuristics for examination timetabling

Journal Article Annals of Operations Research · January 1, 2012 Automating the neighbourhood selection process in an iterative approach that uses multiple heuristics is not a trivial task. Hyper-heuristics are search methodologies that not only aim to provide a general framework for solving problem instances at differe ... Full text Cite

A new hyper-heuristic implementation in HyFlex: a study on generality

Conference Belgian/Netherlands Artificial Intelligence Conference · December 1, 2011 Cite

A hyper-heuristic approach for assigning patients to hospital rooms

Conference PATAT 2010 - Proceedings of the 8th International Conference on the Practice and Theory of Automated Timetabling · December 1, 2010 Cite

Hyper-heuristics with a dynamic heuristic set for the home care scheduling problem

Conference 2010 IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 IEEE Congress on Evolutionary Computation, CEC 2010 · December 1, 2010 A hyper-heuristic performs search over a set of other search mechanisms. During the search, it does not require any problem-dependent data. This structure makes hyper-heuristics problem-independent indirect search mechanisms. In this study, we propose a le ... Full text Cite

A Reinforcement Learning - Great-Deluge Hyper-Heuristic for Examination Timetabling

Journal Article International Journal of Applied Metaheuristic Computing · January 1, 2010 Hyper-heuristics can be identified as methodologies that search the space generated by a finite set of low level heuristics for solving search problems. An iterative hyper-heuristic framework can be thought of as requiring a single candidate solution an ... Full text Cite

A study of simulated annealing hyperheuristics

Conference 7th International Conference on the Practice and Theory of Automated Timetabling, PATAT 2008 · January 1, 2008 Cite

Learning heuristic selection in hyperheuristics for examination timetabling

Conference 7th International Conference on the Practice and Theory of Automated Timetabling, PATAT 2008 · January 1, 2008 Cite