Partial Shape Matching Using Genetic Algorithms

Shape recognition is a challenging task when images contain overlapping, noisy, occluded, partial shapes. This paper addresses the task of matching input shapes with model shapes described in terms of features such as line segments and angles. The quality of matching is gauged using a measure derived from attributed shape grammars. We apply genetic algorithms to the partial shape-matching task. Preliminary results, using model shapes with 6 to 70 features each, are extremely encouraging

A genetic programming hyper-heuristic for the multidimensional knapsack problem

Purpose: Hyper-heuristics are a class of high-level search techniques which operate on a search space of heuristics rather than directly on a search space of solutions. The purpose of this paper is to investigate the suitability of using genetic programming as a hyper-heuristic methodology to generate constructive heuristics to solve the multidimensional 0-1 knapsack problem. Design/methodology/approach: Early hyper-heuristics focused on selecting and applying a low-level heuristic at each stage of a search. Recent trends in hyper-heuristic research have led to a number of approaches being developed to automatically generate new heuristics from a set of heuristic components. A population of heuristics to rank knapsack items are trained on a subset of test problems and then applied to unseen instances. Findings: The results over a set of standard benchmarks show that genetic programming can be used to generate constructive heuristics which yield human-competitive results. Originality/value: In this work the authors show that genetic programming is suitable as a method to generate reusable constructive heuristics for the multidimensional 0-1 knapsack problem. This is classified as a hyper-heuristic approach as it operates on a search space of heuristics rather than a search space of solutions. To our knowledge, this is the first time in the literature a GP hyper-heuristic has been used to solve the multidimensional 0-1 knapsack problem. The results suggest that using GP to evolve ranking mechanisms merits further future research effort. © Emerald Group Publishing Limited

Surrogate optimization of energy retrofits in domestic building stocks using household carbon valuations

Modelling energy retrofit adoption in domestic urban building stocks is vital for policymakers aiming to reduce emissions. The use of surrogate models to evaluate building performance combined with optimization procedures can optimize small building stocks but are insufficient at the urban scale. Recent methods train neural networks using samples of near-optimal solutions further decreasing the computational cost of optimization. However, these models do not make definitive predictions of decision makers with given environmental preferences. To rectify this, we extend the method by assigning a carbon valuation to households to derive their optimal retrofit solutions. By including the carbon valuation when training the predictive model, we can analyze the impact of households' changing attitudes to emissions. To demonstrate this method we construct an agent-based model of Nottingham, finding that simulated government campaigns to boost environmentalism improve both the number of retrofits performed and the mean emissions reduction of each installation

A constructive approach to examination timetabling based on adaptive decomposition and ordering

In this study, we investigate an adaptive decomposition and ordering strategy that automatically divides examinations into difficult and easy sets for constructing an examination timetable. The examinations in the difficult set are considered to be hard to place and hence are listed before the ones in the easy set in the construction process. Moreover, the examinations within each set are ordered using different strategies based on graph colouring heuristics. Initially, the examinations are placed into the easy set. During the construction process, examinations that cannot be scheduled are identified as the ones causing infeasibility and are moved forward in the difficult set to ensure earlier assignment in subsequent attempts. On the other hand, the examinations that can be scheduled remain in the easy set. Within the easy set, a new subset called the boundary set is introduced to accommodate shuffling strategies to change the given ordering of examinations. The proposed approach, which incorporates different ordering and shuffling strategies, is explored on the Carter benchmark problems. The empirical results show that the performance of our algorithm is broadly comparable to existing constructive approaches

A Decision Support System for Assessing and Prioritizing Sustainable Urban Transportation in Metaverse

Blockchain technology and metaverse advancements allow people to create virtual personalities and spend time online. Integrating public transportation into the metaverse could improve services and collect user data. This study introduces a hybrid decision-making framework for prioritizing sustainable public transportation in Metaverse under q-rung orthopair fuzzy set (q-ROFS) context. In this regard, firstly q-rung orthopair fuzzy (q-ROF) generalized Dombi weighted aggregation operators (AOs) and their characteristics are developed to aggregate the q-ROF information. Second, a q-ROF information-based method using the removal effects of criteria (MEREC) and stepwise weight assessment ratio analysis (SWARA) models are proposed to find the objective and subjective weights of criteria, respectively. Then, a combined weighting model is taken to determine the final weights of the criteria. Third, the weighted sum product (WISP) method is extended to q-ROFS context by considering the double normalization procedures, the proposed operators and integrated weighting model. This method has taken the advantages of two normalization processes and four utility measures that approve the effect of benefit and cost criteria by using weighted sum and weighted product models. Next, to demonstrate the practicality and effectiveness of the presented method, a case study of sustainable public transportation in metaverse is presented in the context of q-ROFSs. The findings of this study confirms that the proposed model can recommend more feasible performance while facing numerous influencing factors and input uncertainties, and thus, provides a wider range of application

Evaluation and Management of Antrochoanal Polyps

Antrochoanal polyps (ACPs) are benign polypoid lesions arising from the maxillary antrum and they extend into the choana. They occur more commonly in children and young adults, and they are almost always unilateral. The etiopathogenesis of ACPs is not clear. Nasal obstruction and nasal drainage are the most common presenting symptoms. The differential diagnosis should include the causes of unilateral nasal obstruction. Nasal endoscopy and computed tomography scans are the main diagnostic techniques, and the treatment of ACPs is always surgical. Functional endoscopic sinus surgery (FESS) and powered instrumentation during FESS for complete removal of ACPs are extremely safe and effective procedures. Physicians should focus on detecting the exact origin and extent of the polyp to prevent recurrence

Multi-objective evolutionary algorithms and hyper-heuristics for wind farm layout optimisation

Wind farm layout optimisation is a challenging real-world problem which requires the discovery of trade-off solutions considering a variety of conflicting criteria, such as minimisation of the land area usage and maximisation of energy production. However, due to the complexity of handling multiple objectives simultaneously, many approaches proposed in the literature often focus on the optimisation of a single objective when deciding the locations for a set of wind turbines spread across a given region. In this study, we tackle a multi-objective wind farm layout optimisation problem. Different from the previously proposed approaches, we are applying a high-level search method, known as selection hyper-heuristic to solve this problem. Selection hyper-heuristics mix and control a predefined set of low-level (meta)heuristics which operate on solutions. We test nine different selection hyper-heuristics including an online learning hyper-heuristic on a multi-objective wind farm layout optimisation problem. Our hyper-heuristic approaches manage three well-known multi-objective evolutionary algorithms as low-level metaheuristics. The empirical results indicate the success and potential of selection hyper-heuristics for solving this computationally difficult problem. We additionally explore other objectives in wind farm layout optimisation problems to gain a better understanding of the conflicting nature of those objectives

Timetabling using a Steady State Genetic Algorithm

In this paper, we present a steady state genetic algorithm (SSGA) for solving a multi-constraint university course-timetabling problem. A configurable tool has been developed, named TEDI (Timetabling Tool for Educational Institutions) for the Faculty of Engineering and Architecture (FEA) at Yeditepe University (YU) using SSGA. TEDI includes a powerful and interactive graphical user interface (GUI) for entering input data and viewing the output. 18 different constraints are identified for university timetabling. No distinction is made between hard and soft constraints. Several new crossover and mutation operators are tested. A new successful mutation operator, named Ranking Mutation Operator (RMO), has been introduced. Initial experimental results are promising

Shape matching using genetic algorithms

This dissertation presents an approach for shape matching that is based on genetic algorithms (GAs). Shape recognition is a challenging task, especially for shapes of objects that are occluded, or touch or overlap with other objects. In our approach the problem of shape matching is viewed as an optimization problem. We use attributed strings to represent shapes. This dissertation addresses the task of shape matching rather than issues related to preprocessing and feature extraction. Different GA operators and different selection procedures are compared. A variety of tests is performed to evaluate the robustness of GA with small and large databases. The GA approach is compared to simulated annealing and \u27memetic\u27 annealing, which is an extension of simulated annealing with hill climbing. A new population based approach called Particle Swarm Optimization (PSO) is analyzed and a discrete version is proposed. This approach is also compared with the GA. Experimental results show that the steady state GA using all operators (crossover, mutation and hill climbing) performs best

Special issue on hyper-heuristics in search and optimization

First paragraph: A hyper-heuristic is an automated methodology for selecting or generating heuristics to solve hard computational search problems. The main feature distinguishing these methods is that they explore a search space of heuristics (rather than a search space of potential solutions to a problem). The goal is that hyper-heuristics will lead to more general systems that are able to automatically operate over a wider range of problem domains than is possible today. The term hyper-heuristic was first used in 1997 to describe a protocol that combines several artificial intelligence methods in the context of automated theorem proving. The term was independently used in 2000 to describe 'heuristics to choose heuristics' in the context of combinatorial optimization. The idea of automating the design of heuristics, however, can be traced back to the early 60s. A more recent research trend in hyper-heuristics attempts to automatically generate new heuristics suited to a given problem or class of problems. This is typically done by combining, through the use of genetic programming for example, components or building-blocks of human designed heuristics