Evaluation of a Permutation-Based Evolutionary Framework for Lyndon Factorizations

String factorization is an important tool for partitioning data for parallel processing and other algorithmic techniques often found in the context of big data applications such as bioinformatics or compression. Duval’s well-known algorithm uniquely factors a string over an ordered alphabet into Lyndon words, i.e., patterned strings which arestrictly smaller than all of their cyclic rotations. While Duval’s algorithm produces a pre-determined factorization, modern applications motivate the demand for factorizations with specific properties, e.g., those that minimize the number of factors or consist of factors with similar lengths. In this paper, we consider the problem of finding an alphabet ordering that yields a Lyndon factorization with such properties. We introduce a flexible evolutionary framework and evaluate it on biological sequence data. For the minimization case, we also propose a new problem-specific heuristic, Flexi-Duval, and a problem-specific mutation operator for Lyndon factorization. Our results show that our framework is competitive with Flexi-Duval for minimization and yields high quality and robust solutions for balancing where no problem-specific algorithm is available

Analysis of the (1+1) EA on LeadingOnes with Constraints

Understanding how evolutionary algorithms perform on constrained problems has gained increasing attention in recent years. In this paper, we study how evolutionary algorithms optimize constrained versions of the classical LeadingOnes problem. We first provide a run time analysis for the classical (1+1) EA on the LeadingOnes problem with a deterministic cardinality constraint, giving $\Theta(n (n-B)\log(B) + n^2)$ as the tight bound. Our results show that the behaviour of the algorithm is highly dependent on the constraint bound of the uniform constraint. Afterwards, we consider the problem in the context of stochastic constraints and provide insights using experimental studies on how the ($\mu$+1) EA is able to deal with these constraints in a sampling-based setting

Enhanced selection method for genetic algorithm to solve traveling salesman problem

Genetic algorithms (GAs) have been applied by many researchers to get an optimized solution for hard problems such as Traveling Salesman Problem (TSP). The selection method in GA plays a significant role in the runtime to get the optimized solution as well as in the quality of the solution. Stochastic Universal Selection (SUS) is one of the selection methods in GA which is considered fast but it leads to lower quality solution.Although using Rank Method Selection (RMS) may lead to high quality solution, it has long runtime.In this work, an enhanced selection method is presented which maintains both fast runtime and high solution quality.First, we present a framework to solve TSP using GA with the original selection method SUS. Then, the SUS is replaced by the proposed enhanced selection method.The experimental results show that a better quality solution was obtained by using the proposed enhanced selection method compared to the original SUS

Evolutionary Multi-Objective Optimization for the Dynamic Knapsack Problem

Evolutionary algorithms are bio-inspired algorithms that can easily adapt to changing environments. In this paper, we study single- and multi-objective baseline evolutionary algorithms for the classical knapsack problem where the capacity of the knapsack varies over time. We establish different benchmark scenarios where the capacity changes every $\tau$ iterations according to a uniform or normal distribution. Our experimental investigations analyze the behavior of our algorithms in terms of the magnitude of changes determined by parameters of the chosen distribution, the frequency determined by $\tau$, and the class of knapsack instance under consideration. Our results show that the multi-objective approaches using a population that caters for dynamic changes have a clear advantage in many benchmarks scenarios when the frequency of changes is not too high. Furthermore, we demonstrate that the distribution handling techniques in advance algorithms such as NSGA-II and SPEA2 do not necessarily result in better performance and even prevent these algorithms from finding good quality solutions in comparison with simple multi-objective approaches

Optimiranje evolucijskim algoritmima

Ovaj rad bavi se pružanjem teorijskog uvoda o radu evolucijskih algoritama (EA) kao algoritama koji se mogu upotrijebiti za optimizaciju brojnih problema iz inženjerske struke. Područje istraživanja EA-ma nastaje unutar područja strojnog učenja kojem je cilj osposobljavanje računala za samostalno programiranje. Inspiraciju u svojem radu, EA-mi posuđuju od biološke evolucije prema teoriji Charlesa Darwina. Osnovnim skupinama EA-ma smatraju se: evolucijske strategije (ES), evolucijsko programiranje (EP), genetski algoritmi (GA) te genetsko programiranje (GP). Iznesena teorija o radu algoritama može se upotrijebiti za rješavanje vrlo velikog broja problema kod kojih je moguća primjena nekih od spomenutih algoritama, a upotreba navedene teorije demonstrirana je na primjerima rada GA i GP algoritma koji su dani na završetku ovoga rada

Cheap IR Evaluation: Fewer Topics, No Relevance Judgements, and Crowdsourced Assessments

To evaluate Information Retrieval (IR) effectiveness, a possible approach is to use test collections, which are composed of a collection of documents, a set of description of information needs (called topics), and a set of relevant documents to each topic. Test collections are modelled in a competition scenario: for example, in the well known TREC initiative, participants run their own retrieval systems over a set of topics and they provide a ranked list of retrieved documents; some of the retrieved documents (usually the first ranked) constitute the so called pool, and their relevance is evaluated by human assessors; the document list is then used to compute effectiveness metrics and rank the participant systems. Private Web Search companies also run their in-house evaluation exercises; although the details are mostly unknown, and the aims are somehow different, the overall approach shares several issues with the test collection approach. The aim of this work is to: (i) develop and improve some state-of-the-art work on the evaluation of IR effectiveness while saving resources, and (ii) propose a novel, more principled and engineered, overall approach to test collection based effectiveness evaluation. [...

Developement of Evolution Algorithm for Shop Scheduling problem

Tato diplomová práce je zaměřena na výzkum evolučních algoritmů (EA) v oblasti plánování zakázkové výroby a na vývoj nové strategie za účelem zlepšení výkonu. Sekvenční rovrhovací problem (JSSP) je jedním z nejsložitějších plánovacích problémů a nalezení optimálního řešení je vzhledem ke složitosti velmi obtížné. Byly přezkoumány existující evoluční algoritmy a pro řešení sekvenčního rozvrhovacího problému byl vybrán jeden z široce používaných genetických algoritmů. Pro porovnání efektivnosti EA jsou vygenerovány nejprve Aktivní plány pro pro eta lonové problémy JSSP na základě různých prioritních pravidel . Poté je přezkoumána struktura a hlavní parametry jednoduchého genetického algoritmu (SGA) a na základě toho je v SGA navržena a implementována nová strategie nahrazení (opakovaně použitelná substituční strategie - RSS). Implementace RRS v SGA zlepšuje výsledky a také byl experimentován její dopad na dva různé typy reprezentací chromozomů. Navržený MSGAJO je považován mezi testovanými za nejlepší genetický algoritmus, který dává nejlepší hodnoty promísení pro případy problému JSSP.This thesis is aimed at research of evolution algorithms (EA) in the field of the shop scheduling problems and to develop a new strategy in order to improve the performance. Job shop scheduling problem (JSSP) is one of the most complex scheduling problem and finding the optimal solution is very difficult due to their complexity. Existing evolution algorithms were reviewed and one of the best and widely used genetic algorithm is selected for solving job shop scheduling problem. Active schedules for JSSP were generated based on various dispatching rules with the help of most used problem instances to compare effectiveness of EA. Then the structure and the major parameters of simple genetic algorithm (SGA) is reviewed and based on that a new strategy for replacement (Reusable Replacement Strategy) is proposed and implemented in the SGA. The implementation of RRS in SGA improves the results and also its impact on two different type of chromosome representations were experimented. The developed MSGAJO is concluded to be the best genetic algorithm among tested to give the best makespan values for the JSSP problem instances