The versatility that genetic algorithm (GA) has proved to have for solving different problems, has make it the first choice of researchers to deal with new challenges. Currently, GAs are the most well known evolutionary algorithms, because their intuitive principle of operation and their relatively simple implementation; besides they have the ability to reflect the philosophy of evolutionary co...

Real world optimization problems are often too complex to be solved through analytical means. Evolutionary algorithms, a class of algorithms that borrow paradigms from nature, are particularly well suited to address such problems. These algorithms are stochastic methods of optimization that have become immensely popular recently, because they are derivative-free methods, are not as prone to get...

A multi-condition multi-objective optimization method that can find Pareto front over a defined condition space is developed for the first time using deep reinforcement learning. Unlike conventional methods which perform at single condition, present learns correlations between conditions and optimal solutions. The exclusive capability of examined in solutions novel modified Kursawe benchmark pr...

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...