A Proof That Using Crossover Can Guarantee Exponential Speed-Ups in Evolutionary Multi-Objective Optimisation

Phylogenetic Inference using Evolutionary Multi-objective Optimisation

Evolutionary relationships among species are usually (i) illustrated by means of a phylogenetic tree and (ii) inferred by optimising some measure of fitness, such as the total evolutionary distance between species or the likelihood of the tree (given a model of the evolutionary process and a data set). The combinatorial complexity of inferring the topology of the best tree makes phylogenetic in...

Preferences in Evolutionary Multi-Objective Optimisation

Multi-objective optimisation (MOO) is an important class of problem in engineering. The conflict of objectives in MOO places the issue of compromise in a central position. Since no single solution optimises all objectives, decision-making based on human preference is a part in solving MOO problems. In this paper application of the evolutionary MOO to the dynamic system controller design by use ...

Multi-Speed Gearbox Design Using Multi-Objective Evolutionary Algorithms

Evolutionary Design and Multi–objective Optimisation

In this paper we explore established methods for optimising multi-objective functions whilst addressing the problem of preliminary design. Methods from the literature are investigated and new ones introduced. All methods are evaluated within a collaborative project for whole system airframe design and the basic problems and difficulties of preliminary design methodology are discussed (Cvetković...

Multi-objective Optimisation of Cancer Chemotherapy Using Evolutionary Algorithms

The main objectives of cancer treatment in general, and of cancer chemotherapy in particular, are to eradicate the tumour and to prolong the patient survival time. Traditionally, treatments are optimised with only one objective in mind. As a result of this, a particular patient may be treated in the wrong way if the decision about the most appropriate treatment objective was inadequate. To part...