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Related Experiment Video

Updated: Jun 9, 2026

Using the GAL4-UAS System for Functional Genetics in Anopheles gambiae

Using the GAL4-UAS System for Functional Genetics in Anopheles gambiae

Published on: April 15, 2021

A ripple-spreading genetic algorithm for the aircraft sequencing problem.

Xiao-Bing Hu¹, Ezequiel A Di Paolo

¹School of Engineering, University of Warwick, Coventry, CV4 7AL, United Kingdom. xiaobing.hu@warwick.ac.uk

Evolutionary Computation

|September 3, 2010

Summary

This summary is machine-generated.

A new ripple-spreading genetic algorithm (GA) offers a binary-representation solution for combinatorial problems like aircraft sequencing. This approach overcomes feasibility and memory issues common with traditional permutation-based GAs.

Related Experiment Videos

Last Updated: Jun 9, 2026

Using the GAL4-UAS System for Functional Genetics in Anopheles gambiae

Using the GAL4-UAS System for Functional Genetics in Anopheles gambiae

Published on: April 15, 2021

Area of Science:

Operations Research
Computer Science
Artificial Intelligence

Background:

Traditional genetic algorithms (GAs) for combinatorial problems often use permutation representations.
Permutation-based GAs face challenges with feasibility and memory efficiency, particularly in complex problems like the aircraft sequencing problem (ASP).
Existing GAs for ASP typically rely on aircraft landing order, which can be limiting.

Purpose of the Study:

To introduce a novel binary-representation-based genetic algorithm scheme for combinatorial problems.
To address the feasibility and memory-efficiency limitations of existing GAs.
To develop a new approach for the aircraft sequencing problem (ASP) using a ripple-spreading model.

Main Methods:

Developed a novel ripple-spreading model to transform landing-order-based ASP solutions into value-based ones.
Projected arriving aircraft as points in an artificial space.
Utilized a deterministic method, inspired by natural ripple-spreading, to generate landing sequences based on input parameters.
Employed a traditional GA to optimize the parameters of the ripple-spreading model for finding optimal sequences.

Main Results:

The proposed ripple-spreading genetic algorithm (RSGA) scheme effectively utilizes binary representations.
RSGA demonstrates advantages in feasibility and memory efficiency compared to traditional permutation-based GAs for ASP.
Extensive comparative studies validated the effectiveness of the RSGA for the aircraft sequencing problem.

Conclusions:

The ripple-spreading genetic algorithm (RSGA) provides an effective and efficient alternative for solving combinatorial problems.
The novel ripple-spreading model successfully overcomes limitations associated with permutation representations in GAs.
RSGA offers a promising approach for optimizing complex sequencing tasks like aircraft sequencing.