Conservation is an ethic of sustainable use of natural resources which focuses on the preservation of biodiversity, i.e., the degree of variation of life. Conservation planning seeks to reach this goal by means of deliberate actions, aimed at the protection (or restoration) of biodiversity features. In this paper we present an intelligent system to assist conservation managers in planning habitat restoration actions, with focus on the activities to be carried out in the islands of the Great Barrier Reef (QLD) and the Pilbara (WA) regions of Australia. In particular, we propose a constrained optimisation formulation of the habitat restoration planning (HRP) problem, capturing aspects such as population dynamics and uncertainty. We show that the HRP is NP-hard, and develop a constraint programming (CP) model and a large neighbourhood search (LNS) procedure to generate activity plans under budgeting constraints.

Conservation is an ethic of sustainable use of natural resources which focuses on the preservation of biodiversity. The term conservation planning encompasses the set of activities, typically carried out by conservation managers, that contribute to the attainment of this goal. Such activities can be preventive, such as the establishment of conservation reserves, or remedial, such as the displacement (or offsetting) of the species to be protected or the culling of invasive species. This last technique is often referred to as habitat restoration and, because of its lower impact on economic activities, is becoming more and more popular among conservation managers. In this paper we present the original formulation of the habitat restoration planning (HRP) problem, which captures some of the decisions and constraints faced by conservation managers in the context of habitat restoration. Example scenarios are drawn from the insular Great Barrier Reef (QLD) and Pilbara (WA) regions of Australia. In addition to the problem formulation, we describe an optimisation solver for the HRP, based on genetic algorithms (GAs), we discuss the preliminary results obtained by our solver, and we outline the current and future directions for the project.

In genetic programming, the size of a solution is typically not specified in advance, and solutions of larger size may have a larger benefit. The flexibility often comes at the cost of the so-called bloat problem: individuals grow without providing additional benefit to the quality of solutions, and the additional elements can block the optimization process. Consequently, problems that are relatively easy to optimize cannot be handled by variable-length evolutionary algorithms. In this article, we analyze different single- and multiobjective algorithms on the sorting problem, a problem that typically lacks independent and additive fitness structures. We complement the theoretical results with comprehensive experiments to indicate the tightness of existing bounds, and to indicate bounds where theoretical results are missing.

In this paper, we concentrate on the problem of finding the viewpoint that best satisfies a set of visual composition properties, often referred to as Virtual Camera or Viewpoint Composition. Previous approaches in the literature, which are based on general optimization solvers, are limited in their practical applicability because of unsuitable computation times and limited experimental analysis. To bring performances much closer to the needs of interactive applications, we introduce novel ways to define visual properties, evaluate their satisfaction, and initialize the search for optimal viewpoints, and test them in several problems under various time budgets, quantifying also, for the first time in the domain, the importance of tuning the parameters that control the behavior of the solving process. While our solver, as others in the literature, is based on Particle Swarm Optimization, our contributions could be applied to any stochastic search process that solves through many viewpoint evaluations, such as the genetic algorithms employed by other papers in the literature. The complete source code of our approach, together with the scenes and problems we have employed, can be downloaded from https://bitbucket.org/rranon/smart-viewpoint-computation-lib.

We consolidate the existing computational complexity analysis of genetic programming (GP) by bringing together sound theoretical proofs and empirical analysis. In particular, we address computational complexity issues arising when coupling algorithms using variable length representation, such as GP itself, with different bloat-control techniques. In order to accomplish this, we first introduce several novel upper bounds for two single- and multi-objective GP algorithms on the generalised Weighted ORDER and MAJORITY problems. To obtain these, we employ well-established computational complexity analysis techniques such as fitness-based partitions, and for the first time, additive and multiplicative drift.