A5074319022- Scheduling and Timetabling Solutions
- Vehicle Routing Optimization Methods
- Metaheuristic Optimization Algorithms Research
- Evolutionary Algorithms and Applications
- Optimization and Packing Problems
- Scheduling and Optimization Algorithms
- Artificial Intelligence in Games
- Constraint Satisfaction and Optimization
- Advanced Manufacturing and Logistics Optimization
- Advanced Multi-Objective Optimization Algorithms
- Artificial Immune Systems Applications
- Complex Systems and Time Series Analysis
- Sports Analytics and Performance
- Reinforcement Learning in Robotics
- Stock Market Forecasting Methods
- Evolutionary Game Theory and Cooperation
- Assembly Line Balancing Optimization
- Transportation and Mobility Innovations
- Computational Geometry and Mesh Generation
- Experimental Behavioral Economics Studies
- Advanced Computational Techniques and Applications
- Intelligent Tutoring Systems and Adaptive Learning
- Game Theory and Applications
- Manufacturing Process and Optimization
- Digital Games and Media
University of Nottingham
2015-2025
Nilai University
2024-2025
University of Nottingham Malaysia Campus
2015-2024
MILA University
2024
Shanghai Jiao Tong University
2021
Universiti of Malaysia Sabah
2020
China University of Mining and Technology
2017
Computational Intelligence and Information Systems Lab
2017
Institut Pprime
2016
École Nationale Supérieure de Mécanique et d'Aérotechnique
2016
This paper presents a new best-fit heuristic for the two-dimensional rectangular stock-cutting problem and demonstrates its effectiveness by comparing it against other published approaches. A placement algorithm usually takes list of shapes, sorted some property such as increasing height or decreasing area, then applies rule to each these shapes in turn. The proposed method is not restricted first shape encountered but may dynamically search better candidate placement. We suggest an...
Examines measures of diversity in genetic programming. The goal is to understand the importance such and their relationship with fitness. Diversity methods from literature are surveyed a selected set applied common standard problem instances an experimental study. Results show varying definitions behaviors correlation between fitness during different stages evolutionary process. Populations programming algorithm shown become structurally similar while maintaining high amount behavioral...
This paper presents a new heuristic algorithm for the two-dimensional irregular stock-cutting problem, which generates significantly better results than previous state of art on wide range established benchmark problems. The developed is able to pack shapes with traditional line representation, and it can also that incorporate circular arcs holes. in itself represents significant improvement upon art. By utilising hill climbing tabu local search methods, proposed technique produces 25 best...
Portfolio optimization involves the optimal assignment of limited capital to different available financial assets achieve a reasonable trade-off between profit and risk objectives. In this paper, we studied extended Markowitz's mean-variance portfolio model. We considered cardinality, quantity, pre-assignment round lot constraints in These four real-world limit number portfolio, restrict minimum maximum proportions held require some specific be included invest units certain size...
Single-player games (often called puzzles) have received considerable attention from the scientific community. Consequently, interesting insights into some puzzles, and approaches for solving them, emerged. However, many puzzl
We present a genetic programming (GP) system to evolve reusable heuristics for the 2-D strip packing problem. The evolved are constructive, and decide both which piece pack next where place that piece, given current partial solution. This paper contributes growing research area represents paradigm shift in search methodologies. Instead of using evolutionary computation space solutions, we employ it A key motivation is investigate methods automate heuristic design process. It has been stated...
Hyper-heuristics are search methodologies that aim to provide high-quality solutions across a wide variety of problem domains, rather than developing tailor-made for each instance/domain. A traditional hyper-heuristic framework has two levels, namely, the high level strategy (heuristic selection mechanism and acceptance criterion) low heuristics (a set specific heuristics). Due different landscape structures instances, plays an important role in design framework. In this paper, we propose...
Hyper-heuristic approaches aim to automate heuristic design in order solve multiple problems instead of designing tailor-made methodologies for individual problems. Hyper-heuristics accomplish this through a high-level (heuristic selection mechanism and an acceptance criterion). This automates selection, deciding whether accept or reject the returned solution. The fact that different problems, even instances, have landscape structures complexity, efficient heuristics can dramatic impact on...
Designing generic problem solvers that perform well across a diverse set of problems is challenging task. In this work, we propose hyper-heuristic framework to automatically generate an effective and solution method by utilizing grammatical evolution. the proposed framework, evolution used as online solver builder, which takes several heuristic components (e.g., different acceptance criteria neighborhood structures) inputs evolves templates perturbation heuristics. The evolved are...