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.

An introduction and survey of estimation of distribution algorithms

“…This model-based approach to optimization has allowed EDAs to solve many large and complex problems. EDAs were successfully applied to optimization of large spin glass instances in two-dimensional and three-dimensional lattices (Pelikan & Hartmann, 2006), military antenna design (Yu, Santarelli, & Goldberg, 2006), multiobjective knapsack (Shah & Reed, 2010), groundwater remediation design (Arst, Minsker, & Goldberg, 2002;Hayes & Minsker, 2005), aminoacid alphabet reduction for protein structure prediction (Bacardit, Stout, Hirst, Sastry, Llorà, & Krasnogor, 2007), identification of clusters of genes with similar expression profiles (Peña, Lozano, & Larrañaga, 2004), economic dispatch (Chen & p. Chen, 2007), forest management (Ducheyne, De Baets, & De Wulf, 2004), portfolio management (Lipinski, 2007), cancer chemotherapy optimization (Petrovski, Shakya, & Mccall, 2006), environmental monitoring network design (Kollat, Reed, & Kasprzyk, 2008), and others. It is important to stress that in most of these applications no other technique was shown to be capable of achieving better performance than EDAs or solving problems of comparable size and complexity.…”

Analysing the Effect of Demand Uncertainty in Dynamic Pricing with EAs

“…EDA is a relatively new area in evolutionary computation field and are being increasingly applied to real-world optimization problems. They are often reported to perform better than the traditional GAs [8] [22]. It is, therefore, interesting to see the performance of both EDA and GA with regards to dynamic pricing.…”

The effects of mutation and directed intervention crossover when applied to scheduling chemotherapy

“…In order to test these techniques, a cancer chemotherapy treatment problem was chosen since GAs have already been successfully used in chemotherapy design problems [15]. This has a similar form to the previous scheduling work as anticancer drugs are generally applied according to a schedule where s doses are given at times t1, t2, .…”