arXiv subjects

G. Iacca

Publications and source records attributed to G. Iacca.

4 recordsLinked to original sources

arXiv2026

Comparing Optimization Models for Radiotherapy Scheduling

The Radiotherapy Scheduling Problem (RTSP) involves determining an optimal schedule for patients undergoing radiation treatments, a task that has a massive impact on clinical outcomes given the central role of radiotherapy in cancer care. The daily batch approach--which consists of scheduling all the newly arrived patients together at the end of each day--modelled with Integer Linear Programming, is currently one of the most effective methods for the RTSP. However, this kind of formulation requires substantial computational resources in terms of time and memory. Here, we address these limitations by developing two novel greedy heuristics (named RTSP First Fit and RTSP Best Fit) and use them as constructive heuristics for a Simulated Annealing (SA) approach to optimize the scheduling. The proposed methods--the heuristics alone and their combination with SA--are evaluated on a publicly available dataset against an integer linear program formulation solved with two different state-of-the-art exact solvers. Evaluation metrics include six scheduling objectives capturing patient waiting times, preference satisfaction, and changes in linear accelerator assignment (aggregated in four different weight configurations), solving time, and memory consumption. The results show that the novel heuristics achieve solutions close to those of exact methods, while dramatically reducing runtime and memory usage; furthermore, when combined with SA, they further improve the solution quality while maintaining low runtime and memory usage.

math.OC↗

arXiv2018

Optimizing groups of colluding strong attackers in mobile urban communication networks with evolutionary algorithms

In novel forms of the Social Internet of Things, any mobile user within communication range may help routing messages for another user in the network. The resulting message delivery rate depends both on the users' mobility patterns and the message load in the network. This new type of configuration, however, poses new challenges to security, amongst them, assessing the effect that a group of colluding malicious participants can have on the global message delivery rate in such a network is far from trivial. In this work, after modeling such a question as an optimization problem, we are able to find quite interesting results by coupling a network simulator with an evolutionary algorithm. The chosen algorithm is specifically designed to solve problems whose solutions can be decomposed into parts sharing the same structure. We demonstrate the effectiveness of the proposed approach on two medium-sized Delay-Tolerant Networks, realistically simulated in the urban contexts of two cities with very different route topology: Venice and San Francisco. In all experiments, our methodology produces attack patterns that greatly lower network performance with respect to previous studies on the subject, as the evolutionary core is able to exploit the specific weaknesses of each target configuration.

cs.NE↗

arXiv2018

Ockham's Razor in Memetic Computing: Three Stage Optimal Memetic Exploration

Memetic Computing is a subject in computer science which considers complex structures as the combination of simple agents, memes, whose evolutionary interactions lead to intelligent structures capable of problem-solving. This paper focuses on Memetic Computing optimization algorithms and proposes a counter-tendency approach for algorithmic design. Research in the field tends to go in the direction of improving existing algorithms by combining different methods or through the formulation of more complicated structures. Contrary to this trend, we instead focus on simplicity, proposing a structurally simple algorithm with emphasis on processing only one solution at a time. The proposed algorithm, namely Three Stage Optimal Memetic Exploration, is composed of three memes; the first stochastic and with a long search radius, the second stochastic and with a moderate search radius and the third deterministic and with a short search radius. The bottom-up combination of the three operators by means of a natural trial and error logic, generates a robust and efficient optimizer, capable of competing with modern complex and computationally expensive algorithms. This is suggestive of the fact that complexity in algorithmic structures can be unnecessary, if not detrimental, and that simple bottom-up approaches are likely to be competitive is here invoked as an extension to Memetic Computing basing on the philosophical concept of Ockham's Razor. An extensive experimental setup on various test problems and one digital signal processing application is presented. Numerical results show that the proposed approach, despite its simplicity and low computational cost displays a very good performance on several problems, and is competitive with sophisticated algorithms representing the-state-of-the-art in computational intelligence optimization.

cs.LG↗

arXiv2018

Memetic Viability Evolution for Constrained Optimization

The performance of evolutionary algorithms can be heavily undermined when constraints limit the feasible areas of the search space. For instance, while Covariance Matrix Adaptation Evolution Strategy is one of the most efficient algorithms for unconstrained optimization problems, it cannot be readily applied to constrained ones. Here, we used concepts from Memetic Computing, i.e. the harmonious combination of multiple units of algorithmic information, and Viability Evolution, an alternative abstraction of artificial evolution, to devise a novel approach for solving optimization problems with inequality constraints. Viability Evolution emphasizes elimination of solutions not satisfying viability criteria, defined as boundaries on objectives and constraints. These boundaries are adapted during the search to drive a population of local search units, based on Covariance Matrix Adaptation Evolution Strategy, towards feasible regions. These units can be recombined by means of Differential Evolution operators. Of crucial importance for the performance of our method, an adaptive scheduler toggles between exploitation and exploration by selecting to advance one of the local search units and/or recombine them. The proposed algorithm can outperform several state-of-the-art methods on a diverse set of benchmark and engineering problems, both for quality of solutions and computational resources needed.

cs.NE↗