On the Runtime of Randomized Local Search and Simple Evolutionary Algorithms for Dynamic Makespan Scheduling

Evolutionary algorithms have been frequently used for dynamic optimization problems. With this paper, we contribute to the theoretical understanding of this research area. We present the first computational complexity analysis of evolutionary algorithms for a dynamic variant of a classical combinatorial optimization problem, namely makespan scheduling. We study the model of a strong adversary which is allowed to change one job at regular intervals. Furthermore, we investigate the setting of random changes. Our results show that randomized local search and a simple evolutionary algorithm are very effective in dynamically tracking changes made to the problem instance.Comment: Conference version appears at IJCAI 201

A Co-optimal Coverage Path Planning Method for Aerial Scanning of Complex Structures

The utilization of unmanned aerial vehicles (UAVs) in survey and inspection of civil infrastructure has been growing rapidly. However, computationally efficient solvers that find optimal flight paths while ensuring high-quality data acquisition of the complete 3D structure remains a difficult problem. Existing solvers typically prioritize efficient flight paths, or coverage, or reducing computational complexity of the algorithm – but these objectives are not co-optimized holistically. In this work we introduce a co-optimal coverage path planning (CCPP) method that simultaneously co-optimizes the UAV path, the quality of the captured images, and reducing computational complexity of the solver all while adhering to safety and inspection requirements. The result is a highly parallelizable algorithm that produces more efficient paths where quality of the useful image data is improved. The path optimization algorithm utilizes a particle swarm optimization (PSO) framework which iteratively optimizes the coverage paths without needing to discretize the motion space or simplify the sensing models as is done in similar methods. The core of the method consists of a cost function that measures both the quality and efficiency of a coverage inspection path, and a greedy heuristic for the optimization enhancement by aggressively exploring the viewpoints search spaces. To assess the proposed method, a coverage path quality evaluation method is also presented in this research, which can be utilized as the benchmark for assessing other CPP methods for structural inspection purpose. The effectiveness of the proposed method is demonstrated by comparing the quality and efficiency of the proposed approach with the state-of-art through both synthetic and real-world scenes. The experiments show that our method enables significant performance improvement in coverage inspection quality while preserving the path efficiency on different test geometries

10361 Abstracts Collection and Executive Summary -- Theory of Evolutionary Algorithms

From September 5 to 10, the Dagstuhl Seminar 10361 ``Theory of Evolutionary Algorithms \u27\u27 was held in Schloss Dagstuhl~--~Leibniz Center for Informatics. During the seminar, several participants presented their current research, and ongoing work and open problems were discussed. Abstracts of the presentations given during the seminar as well as abstracts of seminar results and ideas are put together in this paper. The first section describes the seminar topics and goals in general

A heuristic approach to flood evacuation planning

Flood evacuation planning models are an important tool used in preparation for flooding events. Authorities use the plans generated by flood evacuation models to evacuate the population as quickly as possible. Contemporary models consider the whole solution space and use a stochastic search to explore and produce solutions. The one issue with stochastic approaches is that they cannot guarantee the optimality of the solution and it is important that the plans be of a high quality. We present a heuristically driven flood evacuation planning model; the proposed heuristic is deterministic, which allows the model to avoid this problem. The determinism of the model means that the optimality of solutions found can be readily verified

A Survey on Compiler Autotuning using Machine Learning

Since the mid-1990s, researchers have been trying to use machine-learning based approaches to solve a number of different compiler optimization problems. These techniques primarily enhance the quality of the obtained results and, more importantly, make it feasible to tackle two main compiler optimization problems: optimization selection (choosing which optimizations to apply) and phase-ordering (choosing the order of applying optimizations). The compiler optimization space continues to grow due to the advancement of applications, increasing number of compiler optimizations, and new target architectures. Generic optimization passes in compilers cannot fully leverage newly introduced optimizations and, therefore, cannot keep up with the pace of increasing options. This survey summarizes and classifies the recent advances in using machine learning for the compiler optimization field, particularly on the two major problems of (1) selecting the best optimizations and (2) the phase-ordering of optimizations. The survey highlights the approaches taken so far, the obtained results, the fine-grain classification among different approaches and finally, the influential papers of the field.Comment: version 5.0 (updated on September 2018)- Preprint Version For our Accepted Journal @ ACM CSUR 2018 (42 pages) - This survey will be updated quarterly here (Send me your new published papers to be added in the subsequent version) History: Received November 2016; Revised August 2017; Revised February 2018; Accepted March 2018