Real world problems often present multiple, frequently conflicting, objectives. The research for optimal solutions of multi-objective problems can be achieved through means of genetic algorithms, which are inspired by the natural process of evolution: an initial population of solutions is randomly generated, then pairs of solutions are selected and combined in order to create new solutions slig...

Topic Modeling (TM) is a rapidly-growing area at the interfaces of text mining, artificial intelligence and statistical modeling, that is being increasingly deployed to address the ’information overload’ associated with extensive text repositories. The goal in TM is typically to infer a rich yet intuitive summary model of a large document collection, indicating a specific collection of topics t...

In this paper, we propose variational optimistic linear support (VOLS), a novel algorithm that finds bounded approximate solutions for multi-objective coordination graphs (MO-CoGs). VOLS builds and improves upon an existing exact algorithm called variable elimination linear support (VELS). Like VELS, VOLS solves a MO-CoG as a series of scalarized single-objective coordination graphs. We improve...

We describe multi-objective influence diagrams, based on a set of p objectives, where utility values are vectors in R, and are typically only partially ordered. These can still be solved by a variable elimination algorithm, leading to a set of maximal values of expected utility. If the Pareto ordering is used this set can often be prohibitively large. We consider approximate representations of ...

The advancement in power systems has led to the development of generation dispatch (GD) that is difficult to solve by classical optimisation method. The proposed paper work is to evolve simple and effective method for optimum generation dispatch to minimise the fuel cost, environmental cost and security requirement of power networks. The approach is based on the bi-criterion global optimisation...

We describe and evaluate a multi-objective optimisation (MOO) algorithm that works within the Probability Collectives (PC) optimisation framework. PC is an alternative approach to optimization where the optimization process focusses on finding an ideal distribution over the solution space rather than an ideal solution. We describe one way in which MOO can be done in the PC framework, via using ...

Independent component analysis (ICA) is a technique that attempts to separate data into maximally independent groups. Achieving maximal independence in space or time yields two varieties of ICA meaningful for functional MRI (fMRI) applications: spatial ICA (SICA) and temporal ICA (TICA). SICA has so far dominated the application of ICA to fMRI. The objective of these experiments was to study IC...

In this paper we present a family of multi-objective hypergraph partitioning algorithms based on the multilevel paradigm, which are capable of producing solutions in which both the cut and the maximum subdomain degree are simultaneously minimized. This type of partitionings are critical for existing and emerging applications in VLSI CAD as they allow to both minimize and evenly distribute the i...

Good algorithms exist for solving the 2D rectangular strip packing problem when the objective is to minimize the amount of wasted material. However, in some applications other criteria are also important. We describe new heuristics for strip packing that optimize not only for wastage, but also for the efficient use of the cutting equipment, by minimizing the number of independent cuts required ...