Results 1 - 10
of
21
An estimation of distribution algorithm with intelligent local search for rule-based nurse rostering
, 2007
"... ..."
A Survey of NP-Complete Puzzles
, 2008
"... Single-player games (often called puzzles) have received considerable attention from the scientific community. Consequently, interesting insights into some puzzles, and into the approaches for solving them, have emerged. However, many puzzles have been neglected, possibly because they are unknown to ..."
Abstract
-
Cited by 25 (0 self)
- Add to MetaCart
Single-player games (often called puzzles) have received considerable attention from the scientific community. Consequently, interesting insights into some puzzles, and into the approaches for solving them, have emerged. However, many puzzles have been neglected, possibly because they are unknown to many people. In this article, we survey NP-Complete puzzles in the hope of motivating further research in this fascinating area, particularly for those puzzles which have received little scientific attention to date.
The Scalability of Evolved On Line Bin Packing Heuristics
"... The on line bin packing problem concerns the packing of pieces into the least number of bins possible, as the pieces arrive in a sequential fashion. In previous work, we used genetic programming to evolve heuristics for this problem, which beat the human designed ‘best fit’ algorithm. Here we exami ..."
Abstract
-
Cited by 14 (7 self)
- Add to MetaCart
(Show Context)
The on line bin packing problem concerns the packing of pieces into the least number of bins possible, as the pieces arrive in a sequential fashion. In previous work, we used genetic programming to evolve heuristics for this problem, which beat the human designed ‘best fit’ algorithm. Here we examine the performance of the evolved heuristics on larger instances of the problem, which contain many more pieces than the problem instances used in training. In previous work, we concluded that we could confidently apply our heuristics to new instances of the same class of problem. Now we can make the additional claim that we can confidently apply our heuristics to problems of much larger size, not only without deterioration of solution quality, but also within a constant factor of the performance obtained by ‘best fit’. Interestingly, our evolved heuristics respond to the number of pieces in a problem instance although they have no explicit access to that information. We also comment on the important point that, when solutions are explicitly constructed for single problem instances, the size of the search space explodes. However, when working in the space of algorithmic heuristics, the distribution of functions represented in the search space reaches some limiting distribution and therefore the combinatorial explosion can be controlled.
Scheduling English football fixtures over holiday periods
- Journal of the Operational Research Society
, 2008
"... periods ..."
(Show Context)
A Guided Search Genetic Algorithm for the University Course Timetabling Problem
"... Abstract The university course timetabling problem is a combinatorial optimisation problem in which a set of events has to be scheduled in time slots and located in suitable rooms. The design of course timetables for academic institutions is a very difficult task because it is an NP-hard problem. Th ..."
Abstract
-
Cited by 6 (4 self)
- Add to MetaCart
(Show Context)
Abstract The university course timetabling problem is a combinatorial optimisation problem in which a set of events has to be scheduled in time slots and located in suitable rooms. The design of course timetables for academic institutions is a very difficult task because it is an NP-hard problem. This paper proposes a genetic algorithm with a guided search strategy and a local search technique for the university course timetabling problem. The guided search strategy is used to create offspring into the population based on a data structure that stores information extracted from previous good individuals. The local search technique is used to improve the quality of individuals. The proposed genetic algorithm is tested on a set of benchmark problems in comparison with a set of state-of-the-art methods from the literature. The experimental results show that the proposed genetic algorithm is able to produce promising results for the university course timetabling problem. 1
A Multi-Objective Approach for Robust Airline Scheduling
, 2007
"... We present a memetic approach for multi-objective improvement of robustness influencing features (called robustness objectives) in airline schedules. Improvement of the objectives is obtained by making minor incremental changes to the flight schedule- by retiming the flights- and the aircraft rotati ..."
Abstract
-
Cited by 5 (0 self)
- Add to MetaCart
We present a memetic approach for multi-objective improvement of robustness influencing features (called robustness objectives) in airline schedules. Improvement of the objectives is obtained by making minor incremental changes to the flight schedule- by retiming the flights- and the aircraft rotations, subject to a fixed fleet assignment. Approximations of the Pareto optimal front are obtained by applying a multi-meme memetic algorithm. We investigate biased meme selection to encourage exploration of the boundaries of the search space and compare it with random meme selection. An external population of high quality solutions is maintained using the adaptive grid archiving algorithm. The presented approach is applied to investigate simultaneous improvement of reliability and flexibility in real world schedules from KLM Royal Dutch Airlines. Experimental results show that the approach enables us to obtain schedules with significant improvements for the considered objectives. A large scale simulation study was undertaken to quantify the influence of the robustness objectives on the operational performance of the schedules. Rigorous sensitivity analysis of the results shows that the influence of the schedule reliability is dominant and that increased schedule flexibility could improve the operational performance.
Plyasunov A Hybrid Memetic Algorithm for the Competitive p-Median Problem
- Proceedings of INCOM09
, 2009
"... Abstract: In the competitive p-median problem, two decision makers, the leader and the follower, compete to attract clients from a given market. The leader opens his facilities, anticipating that the follower will react to the decision by opening his/her own facilities. The leader and the follower t ..."
Abstract
-
Cited by 4 (3 self)
- Add to MetaCart
Abstract: In the competitive p-median problem, two decision makers, the leader and the follower, compete to attract clients from a given market. The leader opens his facilities, anticipating that the follower will react to the decision by opening his/her own facilities. The leader and the follower try to maximize their own profits. This is the Stackelberg game. We present it as a linear bilevel 0–1 problem. It is known that the problem is ΣP2-complete. We develop a hybrid memetic algorithm for it where the follower problem is solved by commercial software. To obtain an upper bound for this maximization problem, we reformulate the bilevel problem as a single level mixed integer programming problem with exponential number of constraints and variables. Removing some of them, we get the desired upper bound. For finding an appropriate family of constraints and variables, we use metaheuristics again. As a result, we get near optimal solutions for the bilevel problem with an a posteriori bound for deviation from the global optimum. Computational results for Euclidian test instances are discussed.
Directed Intervention Crossover Approaches in Genetic Algorithms with Application to Optimal Control Problems
, 2009
"... Genetic Algorithms (GAs) are a search heuristic technique modelled on the processes of evolution. They have been used to solve optimisation problems in a wide variety of fields. When applied to the optimisation of intervention schedules for optimal control problems, such as cancer chemotherapy treat ..."
Abstract
-
Cited by 2 (1 self)
- Add to MetaCart
(Show Context)
Genetic Algorithms (GAs) are a search heuristic technique modelled on the processes of evolution. They have been used to solve optimisation problems in a wide variety of fields. When applied to the optimisation of intervention schedules for optimal control problems, such as cancer chemotherapy treatment scheduling, GAs have been shown to require more fitness function evaluations than other search heuristics to find fit solutions. This thesis presents extensions to the GA crossover process, termed directed intervention crossover techniques, that greatly reduce the number of fitness function evaluations required to find fit solutions, thus increasing the effectiveness of GAs for problems of this type. The directed intervention crossover techniques use intervention scheduling information from parent solutions to direct the offspring produced in the GA crossover process towards more promising areas of a search space. By counting the number of interventions present in parents and adjusting the number of interventions for offspring schedules around it, this allows for highly fit solutions to be found in less fitness function evaluations. The validity of these novel approaches is illustrated through comparison with conventional GA crossover approaches for optimisation of intervention schedules of bio-control application in mushroom farming and cancer chemotherapy treatment. These involve optimally scheduling the application of a bio-control agent to combat pests in mushroom farming and optimising the timing and dosage strength of cancer chemotherapy treatments to
Performance of genetic algorithms in search for water . . .
"... Purpose We examine the performance of genetic algorithms (GAs) coupled to DFT calculations in uncovering solar water light splitters over a space of almost 19000 perovskite materials. Our goal is to determine whether GAs might allow large chemical spaces to be screened within a reasonable computati ..."
Abstract
-
Cited by 1 (0 self)
- Add to MetaCart
Purpose We examine the performance of genetic algorithms (GAs) coupled to DFT calculations in uncovering solar water light splitters over a space of almost 19000 perovskite materials. Our goal is to determine whether GAs might allow large chemical spaces to be screened within a reasonable computational budget. Methods The entire search space was previously calculated using density functional theory to determine solutions that fulfill constraints on stability, band gap, and band edge position. Here, we test over 2500 GA parameterizations in finding these solutions. Results We find that the best genetic algorithms tested offer almost a 6 times efficiency gain over random search, and are comparable to the performance of a search based on informed chemical rules. In addition, the GA is almost 10 times as efficient as random search in finding half the solutions in the search space. The performance of the GA can be further improved to approximately 12-17 times better performance than random
