Expected Fitness Gains of Randomized Search Heuristics for the Traveling Salesperson Problem.

Randomized search heuristics are frequently applied to NP-hard combinatorial optimization problems. The runtime analysis of randomized search heuristics has contributed tremendously to their theoretical understanding. Recently, randomized search heuristics have been examined regarding their achievable progress within a fixed time budget. We follow this approach and present a fixed budget analysis for an NP-hard combinatorial optimization problem. We consider the well-known Traveling Salesperson problem (TSP) and analyze the fitness increase that randomized search heuristics are able to achieve within a given fixed time budget. In particular, we analyze Manhattan and Euclidean TSP instances and Randomized Local Search (RLS), (1 + 1) EA and (1 + λ) EA algorithms for the TSP in a smoothed complexity setting and derive the lower bounds of the expected fitness gain for a specified number of generations

Towards a Theory-Guided Benchmarking Suite for Discrete Black-Box Optimization Heuristics: Profiling (1+λ)(1+\lambda) EA Variants on OneMax and LeadingOnes

Theoretical and empirical research on evolutionary computation methods complement each other by providing two fundamentally different approaches towards a better understanding of black-box optimization heuristics. In discrete optimization, both streams developed rather independently of each other, but we observe today an increasing interest in reconciling these two sub-branches. In continuous optimization, the COCO (COmparing Continuous Optimisers) benchmarking suite has established itself as an important platform that theoreticians and practitioners use to exchange research ideas and questions. No widely accepted equivalent exists in the research domain of discrete black-box optimization. Marking an important step towards filling this gap, we adjust the COCO software to pseudo-Boolean optimization problems, and obtain from this a benchmarking environment that allows a fine-grained empirical analysis of discrete black-box heuristics. In this documentation we demonstrate how this test bed can be used to profile the performance of evolutionary algorithms. More concretely, we study the optimization behavior of several $(1+\lambda)$ EA variants on the two benchmark problems OneMax and LeadingOnes. This comparison motivates a refined analysis for the optimization time of the $(1+\lambda)$ EA on LeadingOnes

Two-Echelon Vehicle and UAV Routing for Post-Disaster Humanitarian Operations with Uncertain Demand

Humanitarian logistics service providers have two major responsibilities immediately after a disaster: locating trapped people and routing aid to them. These difficult operations are further hindered by failures in the transportation and telecommunications networks, which are often rendered unusable by the disaster at hand. In this work, we propose two-echelon vehicle routing frameworks for performing these operations using aerial uncrewed autonomous vehicles (UAVs or drones) to address the issues associated with these failures. In our proposed frameworks, we assume that ground vehicles cannot reach the trapped population directly, but they can only transport drones from a depot to some intermediate locations. The drones launched from these locations serve to both identify demands for medical and other aids (e.g., epi-pens, medical supplies, dry food, water) and make deliveries to satisfy them. Specifically, we present two decision frameworks, in which the resulting optimization problem is formulated as a two-echelon vehicle routing problem. The first framework addresses the problem in two stages: providing telecommunications capabilities in the first stage and satisfying the resulting demands in the second. To that end, two types of drones are considered. Hotspot drones have the capability of providing cell phone and internet reception, and hence are used to capture demands. Delivery drones are subsequently employed to satisfy the observed demand. The second framework, on the other hand, addresses the problem as a stochastic emergency aid delivery problem, which uses a two-stage robust optimization model to handle demand uncertainty. To solve the resulting models, we propose efficient and novel solution approaches

Improved Fixed-Budget Results via Drift Analysis

Fixed-budget theory is concerned with computing or bounding the fitness value achievable by randomized search heuristics within a given budget of fitness function evaluations. Despite recent progress in fixed-budget theory, there is a lack of general tools to derive such results. We transfer drift theory, the key tool to derive expected optimization times, to the fixed-budged perspective. A first and easy-to-use statement concerned with iterating drift in so-called greed-admitting scenarios immediately translates into bounds on the expected function value. Afterwards, we consider a more general tool based on the well-known variable drift theorem. Applications of this technique to the LeadingOnes benchmark function yield statements that are more precise than the previous state of the art.Comment: 25 pages. An extended abstract of this paper will be published in the proceedings of PPSN 202

The Sampling-and-Learning Framework: A Statistical View of Evolutionary Algorithms

Evolutionary algorithms (EAs), a large class of general purpose optimization algorithms inspired from the natural phenomena, are widely used in various industrial optimizations and often show excellent performance. This paper presents an attempt towards revealing their general power from a statistical view of EAs. By summarizing a large range of EAs into the sampling-and-learning framework, we show that the framework directly admits a general analysis on the probable-absolute-approximate (PAA) query complexity. We particularly focus on the framework with the learning subroutine being restricted as a binary classification, which results in the sampling-and-classification (SAC) algorithms. With the help of the learning theory, we obtain a general upper bound on the PAA query complexity of SAC algorithms. We further compare SAC algorithms with the uniform search in different situations. Under the error-target independence condition, we show that SAC algorithms can achieve polynomial speedup to the uniform search, but not super-polynomial speedup. Under the one-side-error condition, we show that super-polynomial speedup can be achieved. This work only touches the surface of the framework. Its power under other conditions is still open

Challenges for future food systems: from the Green Revolution to food supply chains with a special focus on sustainability

Finding a food system to feed the growing worldwide population remains a challenge, especially in the current era, where natural resources are being dramatically depleted. From a historical point of view, the Green Revolution, together with biofortification and sustainable intensification, was established as a possible solution to counter hunger and malnutrition during the second half of the 20th century. As a solution, to overcome the limitations attributed to the Green Revolution, food supply chains were developed. The current food system, based on the long food supply chain (LFSC), is characterized by globalization, promoting several advantages for both producers and consumers. However, LFSC has been demonstrated to be unable to feed the global population and, furthermore, it generates negative ecological, environmental, logistical, and nutritional pressures. Thus, novel efficient food systems are required to respond to current environmental and consumers' demands, as is the case of short food supply chain (SFSC). As a recently emerging food system, the evaluation of SFSC sustainability in terms of environmental, economic, and social assessment is yet to be determined. This review is focused on the evolution of food supply systems, starting from the Green Revolution to food supply chains, providing a significant perspective on sustainability.The research leading to these results was supported by MICINN supporting the Ramón y Cajal grant for M. A. Prieto (RYC-2017-22891), the Juan de la Cierva Incorporación for Hui Cao (IJC2020-04605- 5-I) and the FPU grant for A. Soria-Lopez (FPU2020/06140); by Xunta de Galicia for supporting the program (EXCELENCIA-ED431F 2020/12) and by supporting the postdoctoral grant of M. Fraga- Corral (ED481B-2019-096) and the predoctoral grants of M. Carpena (ED481A 2021/313) and of P. Garcia-Oliveira (ED481A-2019/295); and by the European Union through the “NextGenerationEU” program supporting the “Margarita Salas” grant awarded to P. Garcia-Perez. The authors are grateful to Ibero-American Program on Science and Technology (CYTED—AQUA-CIBUS, P317RT0003), to the Bio Based Industries Joint Undertaking (JU) under grant agreement No. 888003 UP4HEALTH Project (H2020-BBI-JTI-2019) that supports the work of P. Otero and P. Garcia-Perez. The JU receives support from the European Union’s Horizon 2020 research and innovation program and the Bio Based Industries Consortium. The project SYSTEMIC Knowledge hub on Nutrition and Food Security, has received funding from national research funding parties in Belgium (FWO), France (INRA), Germany (BLE), Italy (MIPAAF), Latvia (IZM), Norway (RCN), Portugal (FCT), and Spain (AEI) in a joint action of JPI HDHL, JPI-OCEANS and FACCE-JPI launched in 2019 under the ERA-NET ERA-HDHL (No. 696295)