All Outputs (4)

Evolutionary computing frameworks for optimisation (2017)
Journal Article
Simons, C., & Ramirez, A. (2017). Evolutionary computing frameworks for optimisation

Evolutionary algorithms can find optimal solutions to problems. This article gives an overview of some programming frameworks available to solve optimisation problems.

Metaheuristic design patterns - new perspectives for larger-scale search architectures (2017)
Book Chapter
Krawiec, K., Simons, C., Swan, J., & Woodward, J. (2017). Metaheuristic design patterns - new perspectives for larger-scale search architectures. In P. Vasant, S. Z. Alparslan-Gok, & G. Weber (Eds.), Handbook of Research on Emergent Applications of Optimization Algorithms (1-36). IGI Global Publishing. https://doi.org/10.4018/978-1-5225-2990-3.ch001

Design patterns capture the essentials of recurring best practice in an abstract form. Their merits are well established in domains as diverse as architecture and software development. They offer significant benefits, not least a common conceptual... Read More about Metaheuristic design patterns - new perspectives for larger-scale search architectures.

Evolutionary computing frameworks for optimisation (2017)
Presentation / Conference
Ramirez, A., & Simons, C. (2017, September). Evolutionary computing frameworks for optimisation. Presented at Meeting of Bristol Branch of the Association of C and C++ Users (ACCU), Bristol, UK

Presentation on frameworks for optimisation using evolutionary computing.

Machine learning with Python (2017)
Presentation / Conference
Ferreira, P., & Simons, C. (2017, April). Machine learning with Python. Presented at 2017 Conference of the Association of C and C++ Users (ACCU 2017), Bristol, UK

This presentation is a case study taken from the travel and holiday industry. Paxport/Multicom, based in UK and Sweden, have recently adopted a recommendation system for holiday accommodation bookings. Machine learning techniques such as Collaborativ... Read More about Machine learning with Python.