Since the cost of software testing has continuously accounted for large proportion of the software development total cost, automatic test data generation becomes a hot topic in recent software testing research. These researches attempt to reduce the cost of software testing by generating test data automatically, but they are discussed only for the single version programs not for the programs which are needed re-testing after changing. On the other hand, the regression testing researches discuss about how to re-test programs after changing, but they don¡¦t talk about how to generate test data automatically. Therefore, we propose an automatic test data evolution strategy in this paper. We use the method of regression testing to find out the part of programs which need re-testing, then automatic evolutes the test data by hybrid genetic algorithm. According to the experiment result, our strategy has the same or better testing ability but needs less cost than the other strategies.

A novel decomposition method that is referred to as Contribution-based Decomposition is presented in this thesis. The influence of variables on the values of objective functions and/ or constraints is interpreted as their contributions. Based on contributions of variables, a design problem is decomposed into a number of sub-problems so that variables have similar relative contributions within each sub-problem. The similarity in contributions among variables will lead to an even pressure on the variables when they are driven to better solutions during an optimisation process and, as a result, better solutions can be obtained. Due to nonlinearity of objectives and/ or constrains, variables??? contributions may vary significantly during the solution process. To cope with such variations, a Dynamic Contribution-based Decomposition (DCD) is proposed. By employing DCD, decomposition of system problems is carried out not only at the beginning, but also during the optimisation process, and as a result, the decomposition will always be consistent with the contributions of the current solutions. Further more, a random decomposition is also developed and presented to work in conjunction with the Dynamic Contribution-based Decomposition to introduce re-decompositions when it is required, aiming to increase the global exploring ability. To solve multidisciplinary engineering optimisation problems more efficiently, new solvers are also developed. These include a mixed discrete variable Pattern Search (MDVPS) algorithm and a mixed discrete variable Genetic Algorithm (MDVGA). Inside the MDVGA, new techniques including a flexible floating-point encoding method, a non-dominance ranking strategy and heuristic crossover and mutation operators are also developed to avoid premature convergence and enhance the GA???s search ability. Both MDVPS and MDVGA are able to handle optimisation problems having mixed discrete variables. The former algorithm is more capable of local searching and the latter has better global search ability. A hybrid solver is proposed, which incorporates the MDVPS and the MDVGA and takes advantage of both their strengths. Lastly, a Dynamic Sub-space Optimisation (DSO) method is developed by employing the proposed Dynamic Contribution-based Decomposition methods and the hybrid solver. By employing DSO, decomposed sub-problems can be solved without explicit coordination. To demonstrate the capability of the proposed methods and algorithms, a range of test problems have been exercised and the results are documented. Collectively the results show significant improvements over other published popular approaches.

Traveling salesman problem (TSP) is a well-known NP-hard problem which can not be solved within a polynomial bounded computation time. However, genetic algorithm (GA) is a familiar heuristic algorithm to obtain near-optimal solutions within reasonable time for TSPs. In TSPs, the geometric properties are problem specific knowledge can be used to enhance GAs. Some tour segments (edges) of TSPs are fine while some maybe too long to appear in a short tour. Therefore, this information can help GAs to pay more attention to fine tour segments and without considering long tour segments as often. Consequently, we propose a new algorithm, called intelligent-OPT hybrid genetic algorithm (IOHGA), to exploit local optimal tour segments and enhance the searching process in order to reduce the execution time and improve the quality of the offspring. The local optimal tour segments are assigned higher priorities for the selection of tour segments to be appeared in a short tour. By this way, tour segments of a TSP are divided into two separate sets. One is a candidate set which contains the candidate fine tour segments and the other is a non-candidate set which contains non-candidate fine tour segments. According to the priorities of tour segments, we devise two genetic operators, the skewed production (SP) and the fine subtour crossover (FSC). Besides, we combine the traditional GA with 2-OPT local search algorithm but with some modifications. The modified 2-OPT is named the intelligent OPT (IOPT). Simulation study was conducted to evaluate the performance of the IOHGA. The experimental results indicate that generally the IOHGA could obtain near-optimal solutions with less time and higher accuracy than the hybrid genetic algorithm with simulated annealing algorithm and the genetic algorithm using the gene expression algorithm. Thus, the IOHGA is an effective algorithm for solving TSPs. If the case is not focused on the optimal solution, the IOHGA can provide good near-optimal solutions rapidly. Therefore, the IOHGA could be incorporated with some clustering algorithm and applied to mobile agent planning problems (MAP) in a real-time environment.

Automatic test data generation is a hot topic in recent software testing research. Various techniques have been proposed with different emphases. Among them, most of the methods are based on Genetic Algorithms (GA). However, whether it is the best Metaheuristic method for such a problem remains unclear. In this paper, we choose to use another approach which arms a GA with an intensive local searcher (the so-called Memetic Algorithm (MA) according to the recent terminology). The idea of incorporating local searcher is based on the observations from many real-world programs. It turns out the results outperform many other known Metaheuristic methods so far. We argue the needs of local search for software testing in the discussion of the paper.

Evolutionary Algorithms (EAs) have been widely used for the optimisation of both theoretical and real-world non-linear problems, although such optimisation methods have found reasonably limited utilisation in fields outside of the academic domain. While the causality of this limited uptake in non-academic fields falls outside the scope of this thesis, the core focus of this research remains strongly influenced by the notions of solution feasibility and making optimisation methods more accessible for engineers, both factors attributed to low EA adoption rates in the commercial space. This thesis focuses on the application of bespoke heuristic methods to the field of water distribution system optimisation. Water distribution systems are complex entities that are difficult to model and optimise as they consist of many interacting components each with a set of considerations to address, hence it is important for the engineer to understand and assess the behaviour of the system to enable its effective design and optimisation. The primary goal of this research is to assess the impact that incorporating water systems knowledge into an evolution algorithm has on algorithm performance when applied to water distribution network optimisation problems. This thesis describes the development of two heuristics influenced by the practices of water systems engineers when designing water distribution networks with the view to increasing an algorithm’s performance and resultant solution feasibility. By utilising heuristics based on engineering design principles and integrating them into existing EAs, it is found that both engineering feasibility and general algorithmic performance can be notably improved. Firstly the heuristics are applied to a standard single-objective EA and then to a multi-objective genetic algorithm. The algorithms are assessed on a number of water distribution network benchmarks from the literature including real-world based, large scale systems and compared to the standard variants of the algorithms. Following this, a set of extensive experiments are conducted to explore how the inclusion of water systems knowledge impacts the sensitivity of an evolutionary algorithm to parameter variance. It was found that the performance of both engineering inspired algorithms were less sensitive to parameter change than the standard genetic algorithm variant meaning that non-experts in the field of meta-heuristics will potentially be able to get much better performance out of the engineering heuristic based algorithms without the need for specialist evolutionary algorithm knowledge. In addition this research explores the notion that visualisation techniques can provide water system engineers with a greater insight into the operation and behaviour of an evolutionary algorithm. The final section of this thesis presents a novel three-dimensional representation of pipe based water systems and demonstrates a range of innovative methods to convey information to the user. The interactive visualisation system presented not only allows the engineer to visualise the various parameters of a network but also allows the user to observe the behaviour and progress of an iterative optimisation method. Examples of the combination of the interactive visualisation system and the EAs developed in this work are shown to enable the user to track and visualise the actions of the algorithm. The visualisation aggregates changes to the network over an EA run and grants significant insight into the operations of an EA as it is optimising a network. The research presented in this thesis demonstrates the effectiveness of integrating water system engineering expertise into evolutionary based optimisation methods. Not only is solution quality improved over standard methods utilising these new heuristic techniques, but the potential for greater interaction between engineer, problem and optimiser has been established.

No / The Job Shop Scheduling Problem (JSSP) is a hard combinatorial optimisation problem. This paper presents a heuristic-based Genetic Algorithm (GA) or Hybrid Genetic Algorithm (HGA) with the aim of overcoming the GA deficiency of fine tuning of solution around the optimum, and to achieve optimal or near optimal solutions for benchmark JSSP. The paper also presents a detail GA parameter analysis (also called sensitivity analysis) for a wide range of benchmark problems from JSSP. The findings from the sensitivity analysis or best possible parameter combination are then used in the proposed HGA for optimal or near optimal solutions. The experimental results of the HGA for several benchmark problems are encouraging and show that HGA has achieved optimal solutions for more than 90% of the benchmark problems considered in this paper. The presented results will provide a reference for selection of GA parameters for heuristic-based GAs for JSSP.

Esta pesquisa trata o Problema de Programação de Tripulantes (PPT), presente no planejamento operacional das empresas aéreas. O principal objetivo do PPT é atribuir um conjunto de tarefas aos tripulantes, considerando as regulamentações trabalhistas, as regras de segurança e as políticas das empresas, de tal maneira que o custo da tripulação seja mínimo. O PPT é normalmente dividido em dois subproblemas, resolvidos sequencialmente: Problema de Determinação das Viagens (PDV) e Problema de Atribuição de Escalas (PAE). No PDV, determina-se um conjunto de viagens que cubra todos os voos planejados. Em seguida, no PAE, as escalas, compostas pelas viagens escolhidas e outras atividades como folgas, sobreavisos, reservas, treinamentos e férias, são atribuídas aos tripulantes. Esta decomposição justifica-se pela natureza combinatória do PPT, porém não incorpora as disponibilidades e as preferências dos tripulantes em ambos os subproblemas (PDV e PAE), gerando assim custos extras relacionados aos conflitos que surgem durante a atribuição das escalas aos tripulantes no PAE. Além disso, as estimativas de custos adotadas no PDV não possuem caráter global, já que o custo real da programação só pode ser obtido após a atribuição das escalas. O estado da arte envolve a solução integrada do PPT, em que se elimina a necessidade de resolver inicialmente o PDV, provendo assim uma melhor estimativa de custo e uma programação final com melhor qualidade, por considerar os custos da tripulação, as disponibilidades e preferências dos tripulantes de forma global. O problema, no entanto, é NP-Difícil. Assim sendo, a metodologia proposta nesta pesquisa objetiva a solução do PPT de forma integrada, através de um Algoritmo Genético Híbrido (AGH) associado a um procedimento de busca em profundidade, levando em conta as particularidades da legislação brasileira. A metodologia foi testada, com sucesso, para a solução de instâncias baseadas na malha real de uma empresa aérea brasileira. / This master of science research treats the Crew Scheduling Problem (CSP), as part of the airlines operational planning. The main aim of the CSP is to assign a set of tasks to crew members, considering the labor regulations, safety rules and policies of companies, such that the crew cost is minimal. The CSP is divided into two subproblems, solved sequentially: Crew Pairing Problem (CPP) and Crew Rostering Problem (CRP). First, CPP provides a set of pairings that covers all the planned flights. Then, in the CRP, the rosters, encompassing the pairings and other activities such as rest periods, alert duties, reserve duties, training times and vacations, are assigned to the crew members. This decomposition is justified by the combinatorial nature of the CSP, but it not incorporates the crew members availabilities and preferences in both subproblems (CPP and CRP), generating extra costs related to conflicts that arise during the assignment of rosters to the crew members in the CRP. Besides, the costs estimations adopted in the CPP does not have a global character, since the real cost of the global schedule can be only obtained after the assignment of the rosters. The state of the art involves the integrated solution of CSP, where the CPP does not need to be solved, thus providing a better estimated cost and a better schedule quality, considering crew costs and also crew members availabilities and preferences globally. The problem, however, is NP-Hard. Therefore, the methodology proposed in this master of science research aims to obtain an integrated solution of the CSP, through an hybrid algorithm genetic associated with a depth-first search procedure, taking into account the Brazilian legislation. The methodology was tested, with success, to solve instances related a real network of a Brazilian airline.

Esta pesquisa trata o Problema de Programação de Tripulantes (PPT), presente no planejamento operacional das empresas aéreas. O principal objetivo do PPT é atribuir um conjunto de tarefas aos tripulantes, considerando as regulamentações trabalhistas, as regras de segurança e as políticas das empresas, de tal maneira que o custo da tripulação seja mínimo. O PPT é normalmente dividido em dois subproblemas, resolvidos sequencialmente: Problema de Determinação das Viagens (PDV) e Problema de Atribuição de Escalas (PAE). No PDV, determina-se um conjunto de viagens que cubra todos os voos planejados. Em seguida, no PAE, as escalas, compostas pelas viagens escolhidas e outras atividades como folgas, sobreavisos, reservas, treinamentos e férias, são atribuídas aos tripulantes. Esta decomposição justifica-se pela natureza combinatória do PPT, porém não incorpora as disponibilidades e as preferências dos tripulantes em ambos os subproblemas (PDV e PAE), gerando assim custos extras relacionados aos conflitos que surgem durante a atribuição das escalas aos tripulantes no PAE. Além disso, as estimativas de custos adotadas no PDV não possuem caráter global, já que o custo real da programação só pode ser obtido após a atribuição das escalas. O estado da arte envolve a solução integrada do PPT, em que se elimina a necessidade de resolver inicialmente o PDV, provendo assim uma melhor estimativa de custo e uma programação final com melhor qualidade, por considerar os custos da tripulação, as disponibilidades e preferências dos tripulantes de forma global. O problema, no entanto, é NP-Difícil. Assim sendo, a metodologia proposta nesta pesquisa objetiva a solução do PPT de forma integrada, através de um Algoritmo Genético Híbrido (AGH) associado a um procedimento de busca em profundidade, levando em conta as particularidades da legislação brasileira. A metodologia foi testada, com sucesso, para a solução de instâncias baseadas na malha real de uma empresa aérea brasileira. / This master of science research treats the Crew Scheduling Problem (CSP), as part of the airlines operational planning. The main aim of the CSP is to assign a set of tasks to crew members, considering the labor regulations, safety rules and policies of companies, such that the crew cost is minimal. The CSP is divided into two subproblems, solved sequentially: Crew Pairing Problem (CPP) and Crew Rostering Problem (CRP). First, CPP provides a set of pairings that covers all the planned flights. Then, in the CRP, the rosters, encompassing the pairings and other activities such as rest periods, alert duties, reserve duties, training times and vacations, are assigned to the crew members. This decomposition is justified by the combinatorial nature of the CSP, but it not incorporates the crew members availabilities and preferences in both subproblems (CPP and CRP), generating extra costs related to conflicts that arise during the assignment of rosters to the crew members in the CRP. Besides, the costs estimations adopted in the CPP does not have a global character, since the real cost of the global schedule can be only obtained after the assignment of the rosters. The state of the art involves the integrated solution of CSP, where the CPP does not need to be solved, thus providing a better estimated cost and a better schedule quality, considering crew costs and also crew members availabilities and preferences globally. The problem, however, is NP-Hard. Therefore, the methodology proposed in this master of science research aims to obtain an integrated solution of the CSP, through an hybrid algorithm genetic associated with a depth-first search procedure, taking into account the Brazilian legislation. The methodology was tested, with success, to solve instances related a real network of a Brazilian airline.

In this Dissertation, the Vehicle Routing Problem with Drones (VRPD), motivated by the growing interest on Unmanned Aerial Vehicles (UAVs, or Drones) by the industry and their applications in logistics is studied. A pioneer work by (MURRAY; CHU, 2015) shows a combination between UAV and a truck to deliver products, presenting an adaptation to the Traveling Salesman Problem (TSP). After a literature review, an extension of the model from Murray and Chu (2015) we present a model for the problem with multiple vehicles. This model is developed as a Mixed Integer Linear Programming (MILP) problem and solved with the solver CPLEX. A heuristic based on a Hybrid Genetic Algorithm (HGA) is also developed and presented. Our results show that the use of drones reduces the total mileage of the trucks by a significant percentage. / Nessa monografia estuda-se o Problema do Roteamento de Veículos com Drones (PRVD), motivado pelo crescente interesse da indústria em Veículos Aéreos Não Tripulados (VANTs) e suas aplicações em logística. O trabalho pioneiro de (MURRAY; CHU, 2015) mostra uma combinação entre VANT e um caminhão para realização de entregas de produtos, no qual foi proposta uma adaptação do Problema do Caixeiro Viajante (PCV). Após uma revisão de literatura, apresenta-se uma extensão do modelo de Murray and Chu (2015) para o problema com múltiplos veículos. Desenvolveu-se um modelo de Programação Linear Inteira Mista que foi resolvido com o solver CPLEX. Uma heurística basead em um Algoritmo Genético Híbrido também foi desenvolvido e é apresentada. Resultados mostram que a utilização dos VANTs reduzem a quilometragem dos caminhões significativamente.

Conselho Nacional de Desenvolvimento CientÃfico e TecnolÃgico / COSTA, L.H.M. UtilizaÃÃo de um algoritmo genÃtico hÃbrido na operaÃÃo de sistemas de abastecimento de Ãgua com Ãnfase na eficiÃncia energÃtica. Fortaleza, 2010. 146 p. Tese (Doutorado) - Universidade Federal do CearÃ, Fortaleza, 2010. Em geral, as regras operacionais dos Sistemas de Abastecimento de Ãgua (SAAs) visam à garantia da continuidade do abastecimento pÃblico, sem a consideraÃÃo da variaÃÃo da tarifa energÃtica ao longo do dia. Este fato ocasiona o aumento do custo energÃtico gerado pelos motores das bombas em funcionamento. Entretanto, alÃm da utilizaÃÃo eficiente da tarifa energÃtica, outros aspectos devem ser considerados na operaÃÃo de um SAA tais como, a gama de combinaÃÃes possÃveis de regras operacionais, a variaÃÃo da demanda hÃdrica e a manutenÃÃo dos nÃveis dos reservatÃrios e das pressÃes nos pontos de consumo dentro de seus limites prÃestabelecidos. Isto motivou o desenvolvimento desta pesquisa, que tem como objetivo fornecer ao operador condiÃÃes de operacionalidade nas estaÃÃes elevatÃrias do sistema de forma racional, nÃo dependendo somente de sua experiÃncia profissional. Desta forma, apresenta-se neste trabalho um modelo computacional de apoio à tomada de decisÃo com vistas à minimizaÃÃo dos gastos com energia elÃtrica. Para tanto, fundamenta-se na junÃÃo da tÃcnica dos Algoritmos GenÃticos (AGs) e do simulador hidrÃulico EPANET. O AG à responsÃvel pela busca de estratÃgias operacionais com custo energÃtico reduzido, enquanto que a avaliaÃÃo do desempenho hidrÃulico dessas estratÃgias à feita pelo EPANET. AlÃm disso, devido à alta aleatoriedade caracterÃstica dos AGs, foi incorporado ao mesmo um conjunto de algoritmos determinÃsticos visando tornar o processo o menos estocÃstico possÃvel. Com o acoplamento destes algoritmos ao AG padrÃo desenvolveu-se um Algoritmo GenÃtico HÃbrido (AGH). A metodologia proposta foi avaliada por meio de trÃs estudos de casos, sendo dois hipotÃticos e um real, localizado na cidade de OurÃm, em Portugal. Os resultados obtidos nos trÃs estudos de caso demonstram a superioridade do AGH em relaÃÃo ao AG padrÃo, tanto pelo encontro de melhores soluÃÃes, como na reduÃÃo considerÃvel do tempo computacional demandado para tal feito. Finalmente, espera-se que o desenvolvimento dessa metodologia possa contribuir para o uso de modelos de otimizaÃÃo na operaÃÃo de SAAs em tempo real. / COSTA, L.H.M. Use of hybrid genetic algorithm in the operation in water supply system considering energy efficiency. Fortaleza, 2010. 146 p. Thesis (Doctorate) - Federal University of CearÃ, Fortaleza, 2010. In general, operational rules applied to water distribution systems are created to assure continuity of the public water supply, without taking into account variations of the energy costs during a day. This causes an elevation of the energy costs due to the pumps. Furthermore besides rational use of energy by the pumps, there are other aspects which should be considered in order to achieve an optimized operation of a water transmission system, such as the daily variation of the water demand and the requirements regarded minimum and maximum water levels in the tanks and pressure requirements in the nodes of the water network. The objective of the present work is to develop a computer code which will determine on optimized operation rule for the system which will reach minimum costs of energy used by the pumps. The system is based in the use of Genetic Algorithms (GA) and the hydraulic network computer system EPANET. The GA for of the system is responsible for the search for rules of low energy costs and the hydraulic calculations are done by EPANET. Besides, one major innovation proposed by this research is the introduction of the Hybrid Genetic Algorithm which in order to reduce the stochastic standard aspect of the GA. The proposed methodology was applied to three study cases: two hypothetical and one real which was located in the city of the OurÃm, Portugal. The results of these three study cases clearly show the superiority of the hydrid GA over the standard GA. The hybrid GA not only obtained better solution but also took much less time to run. Finally, it is expected that the use of this methodology will lead to more real time applications.