Metaheuristics and combinatorial optimization problems

This thesis will use the traveling salesman problem (TSP) as a tool to help present and investigate several new techniques that improve the overall performance of genetic algorithms (GA). Improvements include a new parent selection algorithm, harem select, that outperforms all other parent selection algorithms tested, some by up to 600%. Other techniques investigated include population seeding, random restart, heuristic crossovers, and hybrid genetic algorithms, all of which posted improvements in the range of 1% up to 1100%. Also studied will be a new algorithm, GRASP, that is just starting to enjoy a lot of interest in the research community and will also been applied to the traveling salesman problem (TSP). Given very little time to run, relative to other popular metaheuristic algorithms, GRASP was able to come within 5% of optimal on several of the TSPLIB maps used for testing. Both the GA and the GRASP algorithms will be compared with commonly used metaheuristic algorithms such as simulated annealing (SA) and reactive tabu search (RTS) as well as a simple neighborhood search - greedy search

Abstract Meaning Representation for Multi-Document Summarization

Generating an abstract from a collection of documents is a desirable capability for many real-world applications. However, abstractive approaches to multi-document summarization have not been thoroughly investigated. This paper studies the feasibility of using Abstract Meaning Representation (AMR), a semantic representation of natural language grounded in linguistic theory, as a form of content representation. Our approach condenses source documents to a set of summary graphs following the AMR formalism. The summary graphs are then transformed to a set of summary sentences in a surface realization step. The framework is fully data-driven and flexible. Each component can be optimized independently using small-scale, in-domain training data. We perform experiments on benchmark summarization datasets and report promising results. We also describe opportunities and challenges for advancing this line of research.Comment: 13 page

Graph Transformer for Graph-to-Sequence Learning

The dominant graph-to-sequence transduction models employ graph neural networks for graph representation learning, where the structural information is reflected by the receptive field of neurons. Unlike graph neural networks that restrict the information exchange between immediate neighborhood, we propose a new model, known as Graph Transformer, that uses explicit relation encoding and allows direct communication between two distant nodes. It provides a more efficient way for global graph structure modeling. Experiments on the applications of text generation from Abstract Meaning Representation (AMR) and syntax-based neural machine translation show the superiority of our proposed model. Specifically, our model achieves 27.4 BLEU on LDC2015E86 and 29.7 BLEU on LDC2017T10 for AMR-to-text generation, outperforming the state-of-the-art results by up to 2.2 points. On the syntax-based translation tasks, our model establishes new single-model state-of-the-art BLEU scores, 21.3 for English-to-German and 14.1 for English-to-Czech, improving over the existing best results, including ensembles, by over 1 BLEU.Comment: accepted by AAAI202

Using Evolving Algorithms to Cryptanalysis Nonlinear Cryptosystems

            في هذا البحث، نتحرى عن استخدام الخوارزميات التطورية (EA's) لتحليل أحد أنظمة التشفير غير الخطية التي تعتمد على وحدة السجلات الزاحفة لتبادل البيانات الخطية (LFSR) باستخدام طريقة هجوم النص المشفر فقط. الخوارزمية الجينية (GA) و خوارزمية خلية النمل ((Ant Colony Optimization (ACO) التي استخدمت في مهاجمة أحد أنظمة التشفير غير الخطية المسماة "Shrinking Generator" باستخدام أطوال مختلفة من النص المشفر وأطوال مختلفة من LFSRs المدمجة أثبتت أدائها الجيد في إيجاد القيم الأولية لل LFSRs المدمجة.            In this paper, new method have been investigated using evolving algorithms (EA's) to cryptanalysis one of the nonlinear stream cipher cryptosystems which depends on the Linear Feedback Shift Register (LFSR) unit by using cipher text-only attack. Genetic Algorithm (GA) and Ant Colony Optimization (ACO) which are used for attacking one of the nonlinear cryptosystems called "shrinking generator" using different lengths of cipher text and different lengths of combined LFSRs. GA and ACO proved their good performance in finding the initial values of the combined LFSRs. This work can be considered as a warning for a stream cipher designer to avoid the weak points, which may be found in the stream cipher, and may be explored by the cryptanalysts. This work can find the optimal solution for text with minimum lengths of 20 characters and 100 iteration were very enough to find the real initial values of key stream