@inproceedings{a62ec9584c744c29ab7fac2181852923,
title = "Optimising cancer chemotherapy using an estimation of distribution algorithm and genetic algorithms",
abstract = "This paper presents a methodology for using heuristic search methods to optimise cancer chemotherapy. Specifically, two evolutionary algorithms - Population Based Incremental Learning (PBIL), which is an Estimation of Distribution Algorithm (EDA), and Genetic Algorithms (GAs) have been applied to the problem of finding effective chemotherapeutic treatments. To our knowledge. EDAs have been applied to fewer real world problems compared to GAs, and the aim of the present paper is to expand the application domain of this technique. We compare and analyse the performance of both algorithms and draw a conclusion as to which approach to cancer chemotherapy optimisation is more efficient and helpful in the decision-making activity led by the oncologists.",
keywords = "Estimation of Distribution Algorithms, Evolutionary Computation, Probabilistic Modelling",
author = "Andrei Petrovski and Siddhartha Shakya and John McCall",
year = "2006",
doi = "10.1145/1143997.1144073",
language = "British English",
isbn = "1595931864",
series = "GECCO 2006 - Genetic and Evolutionary Computation Conference",
publisher = "Association for Computing Machinery (ACM)",
pages = "413--418",
booktitle = "GECCO 2006 - Genetic and Evolutionary Computation Conference",
address = "United States",
note = "8th Annual Genetic and Evolutionary Computation Conference 2006 ; Conference date: 08-07-2006 Through 12-07-2006",
}
