Evolutionary Computation for Real-World Problems

Abstract

In this paper we discuss three topics that are present in the area of realworld optimization, but are often neglected in academic research in evolutionary computation community. First, problems that are a combination of several interacting sub-problems (so-called multi-component problems) are common in many real-world applications and they deserve better attention of research community. Second, research on optimisation algorithms that focus the search on the edges of feasible regions of the search space is important as high quality solutions usually are the boundary points between feasible and infeasible parts of the search space in many real-world problems. Third, finding bottlenecks and best possible investment in real-world processes are important topics that are also of interest in real-world optimization. In this chapter we discuss application opportunities for evolutionary computation methods in these three areas.

Estimation of distribution algorithms for the Multi-Mode Resource Constrained Project scheduling problem

“…We denote the start and finish times of activity i by st i and f t i respectively. The precedence constraint is presented in (1) while the renewable and non-renewable resource constraints are respectively presented in (2) and (3). In (2) and (3), k i is the allocated mode of i and can only be one of the predefined modes of i.…”

“…The precedence constraint is presented in (1) while the renewable and non-renewable resource constraints are respectively presented in (2) and (3). In (2) and (3), k i is the allocated mode of i and can only be one of the predefined modes of i. Also, α i,ki,r and β i,ki,l are respectively the amount of renewable resource r and non-renewable resource l required by activity i performed in mode k i .…”

“…Multi-component problems such as the Multi-Mode Resource Constrained Project Problem (MRCPSP) have been of research interest to the computational intelligence community in recent years [1], [2]. This class of problems consists of two or more sub-problems that cannot be solved in isolation.…”

Advanced mine optimisation under uncertainty using evolution

“…The area of mining where the goal is to extract ore in a cost efficient way poses large scale optimisation problems, and evolutionary computation techniques have successfully been applied in this area [1,19,21,6]. We consider the problem of mine planning and focus on uncertainties which highly impact the mine planning process.…”

Just-in-time batch scheduling problem with two-dimensional bin packing constraints

“…Multi-component problems are increasingly suscitating the interest of Evolutionary Computation (EC) and Operations Research (OR) communities [12,4]. Not only do they combine several combinatorial optimisation aspects into a single problem but they also emanate from the compounded complexity of conflicting issues in areas like logistics and supply chain management.…”