Uniform Crossover in Genetic Algorithms
Proceedings of the 3rd International Conference on Genetic Algorithms
Introduction to Evolutionary Computing
Introduction to Evolutionary Computing
Genetic search methods in air traffic control
Computers and Operations Research
An efficient genetic algorithm with uniform crossover for air traffic control
Computers and Operations Research
A ripple-spreading genetic algorithm for the airport gate assignment problem
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Genetic algorithm based on receding horizon control for arrival sequencing and scheduling
Engineering Applications of Artificial Intelligence
Binary-Representation-Based Genetic Algorithm for Aircraft Arrival Sequencing and Scheduling
IEEE Transactions on Intelligent Transportation Systems
Evolutionary computation: comments on the history and current state
IEEE Transactions on Evolutionary Computation
Clustering-Based Adaptive Crossover and Mutation Probabilities for Genetic Algorithms
IEEE Transactions on Evolutionary Computation
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When genetic algorithms (GAs) are applied to combinatorial problems, permutation representations are usually adopted. As a result, such GAs are often confronted with feasibility and memory-efficiency problems. With the aircraft sequencing problem (ASP) as a study case, this paper reports on a novel binary-representation-based GA scheme for combinatorial problems. Unlike existing GAs for the ASP, which typically use permutation representations based on aircraft landing order, the new GA introduces a novel ripple-spreading model which transforms the original landing-order-based ASP solutions into value-based ones. In the new scheme, arriving aircraft are projected as points into an artificial space. A deterministic method inspired by the natural phenomenon of ripple-spreading on liquid surfaces is developed, which uses a few parameters as input to connect points on this space to form a landing sequence. A traditional GA, free of feasibility and memory-efficiency problems, can then be used to evolve the ripple-spreading related parameters in order to find an optimal sequence. Since the ripple-spreading model is the centerpiece of the new algorithm, it is called the ripple-spreading GA (RSGA). The advantages of the proposed RSGA are illustrated by extensive comparative studies for the case of the ASP.