Aggregated fuzzy answer set programming

Fuzzy Answer Set programming (FASP) is an extension of answer set programming (ASP), based on fuzzy logic. It allows to encode continuous optimization problems in the same concise manner as ASP allows to model combinatorial problems. As a result of its inherent continuity, rules in FASP may be satisfied or violated to certain degrees. Rather than insisting that all rules are fully satisfied, we may only require that they are satisfied partially, to the best extent possible. However, most approaches that feature partial rule satisfaction limit themselves to attaching predefined weights to rules, which is not sufficiently flexible for most real-life applications. In this paper, we develop an alternative, based on aggregator functions that specify which (combination of) rules are most important to satisfy. We extend upon previous work by allowing aggregator expressions to define partially ordered preferences, and by the use of a fixpoint semantics

Machine Learning Guided Exploration of an Empirical Ribozyme Fitness Landscape

Okinawa Institute of Science and Technology Graduate UniversityDoctor of PhilosophyFitness landscape of a biomolecule is a representation of its activity as a function of its sequence. Properties of a fitness landscape determine how evolution proceeds. Therefore, the distribution of functional variants and more importantly, the connectivity of these variants within the sequence space are important scientific questions. Exploration of these spaces, however, is impeded by the combinatorial explosion of the sequence space. High-throughput experimental methods have recently reduced this impediment but only modestly. Better computational methods are needed to fully utilize the rich information from these experimental data to better understand the properties of the fitness landscape. In this work, I seek to improve this exploration process by combining data from massively parallel experimental assay with smart library design using advanced computational techniques. I focus on an artificial RNA enzyme or ribozyme that can catalyze a ligation reaction between two RNA fragments. This chemistry is analogous to that of the modern RNA polymeraseenzymes, therefore, represents an important reaction in the origin of life. In the first chapter, I discuss the background to this work in the context of evolutionary theory of fitness landscape and its implications in biotechnology. In chapter 2, I explore the use of processes borrowed from the field of evolutionary computation to solve optimization problems using real experimental sequence-activity data. In chapter 3, I investigate the use of supervised machine learning models to extract information on epistatic interactions from the dataset collected during multiple rounds of directed evolution. I investigate and experimentally validate the extent to which a deep learning model can be used to guide a completely computational evolutionary algorithm towards distant regions of the fitness landscape. In the final chapter, I perform a comprehensive experimental assay of the combinatorial region explored by the deep learning-guided evolutionary algorithm. Using this dataset, I analyze higher-order epistasis and attempt to explain the increased predictability of the region sampled by the algorithm. Finally, I provide the first experimental evidence of a large RNA ‘neutral network’. Altogether, this work represents the most comprehensive experimental and computational study of the RNA ligase ribozyme fitness landscape to date, providing important insights into the evolutionary search space possibly explored during the earliest stages of life.doctoral thesi

How Good are Genetic Algorithms at Finding Large Cliques: An Experimental Study

This paper investigates the power of genetic algorithms at solving the MAX-CLIQUE problem. We measure the performance of a standard genetic algorithm on an elementary set of problem instances consisting of embedded cliques in random graphs. We indicate the need for improvement, and introduce a new genetic algorithm, the multi-phase annealed GA, which exhibits superior performance on the same problem set. As we scale up the problem size and test on \hard" benchmark instances, we notice a degraded performance in the algorithm caused by premature convergence to local minima. To alleviate this problem, a sequence of modi cations are implemented ranging from changes in input representation to systematic local search. The most recent version, called union GA, incorporates the features of union cross-over, greedy replacement, and diversity enhancement. It shows a marked speed-up in the number of iterations required to find a given solution, as well as some improvement in the clique size found. We discuss issues related to the SIMD implementation of the genetic algorithms on a Thinking Machines CM-5, which was necessitated by the intrinsically high time complexity (O(n3)) of the serial algorithm for computing one iteration. Our preliminary conclusions are: (1) a genetic algorithm needs to be heavily customized to work "well" for the clique problem; (2) a GA is computationally very expensive, and its use is only recommended if it is known to find larger cliques than other algorithms; (3) although our customization e ort is bringing forth continued improvements, there is no clear evidence, at this time, that a GA will have better success in circumventing local minima.NSF (CCR-9204284

OWA-based fuzzy m-ary adjacency relations in Social Network Analysis.

In this paper we propose an approach to Social Network Analysis (SNA) based on fuzzy m-ary adjacency relations. In particular, we show that the dimension of the analysis can naturally be increased and interesting results can be derived. Therefore, fuzzy m-ary adjacency relations can be computed starting from fuzzy binary relations and introducing OWA-based aggregations. The behavioral assumptions derived from the measure and the exam of individual propensity to connect with other suggest that OWA operators can be considered particularly suitable in characterizing such relationships.reciprocal relation; fuzzy preference relation; priority vector; normalization

Permutation based decision making under fuzzy environment using Tabu search

One of the techniques, which are used for Multiple Criteria Decision Making (MCDM) is the permutation. In the classical form of permutation, it is assumed that weights and decision matrix components are crisp. However, when group decision making is under consideration and decision makers could not agree on a crisp value for weights and decision matrix components, fuzzy numbers should be used. In this article, the fuzzy permutation technique for MCDM problems has been explained. The main deficiency of permutation is its big computational time, so a Tabu Search (TS) based algorithm has been proposed to reduce the computational time. A numerical example has illustrated the proposed approach clearly. Then, some benchmark instances extracted from literature are solved by proposed TS. The analyses of the results show the proper performance of the proposed method

Assisted specification of discrete choice models

Determining appropriate utility specifications for discrete choice models is time-consuming and prone to errors. With the availability of larger and larger datasets, as the number of possible specifications exponentially grows with the number of variables under consideration, the analysts need to spend increasing amounts of time on searching for good models through trial-and-error, while expert knowledge is required to ensure these models are sound. This paper proposes an algorithm that aims at assisting modelers in their search. Our approach translates the task into a multi-objective combinatorial optimization problem and makes use of a variant of the variable neighborhood search algorithm to generate sets of promising model specifications. We apply the algorithm both to semi-synthetic data and to real mode choice datasets as a proof of concept. The results demonstrate its ability to provide relevant insights in reasonable amounts of time so as to effectively assist the modeler in developing interpretable and powerful models