Evolutionary Approach for Concurrency Testing of Ripple Blockchain Consensus Algorithm

Blockchain systems are prone to concurrency bugs due to the nondeterminism in the delivery order of messages between the distributed nodes. These bugs are hard to detect since they can only be triggered by a specific order or timing of concurrent events in the execution. <br/>Systematic concurrency testing techniques, which explore all possible...

Searching for Quality: Genetic Algorithms and Metamorphic Testing for Software Engineering ML

More machine learning (ML) models are introduced to the field of Software Engineering (SE) and reached a stage of maturity to be considered for real-world use; But the real world is complex, and testing these models lacks often in explainability, feasibility and computational capacities. Existing research introduced meta-morphic testing to...

Bi-Attempted Base Optimization Algorithm on Optimization of Hydrosystems

This study aims to search for optimum design parameters for a slurry pipeline problem and optimum operation parameters for a multi-reservoir scheduling problem by using Bi-Attempted Base Optimization Algorithm (ABaOA), which has been recently developed as a numerical bidirectional search algorithm. The slurry pipeline problem is a constrained...

Obtaining Smoothly Navigable Approximation Sets in Bi-Objective Multi-Modal Optimization with an Application to Prostate HDR Brachytherapy Automated Treatment Planning

Even if a Multi-modal Multi-Objective Evolutionary Algorithm (MMOEA) is designed to find all locally optimal approximation sets of a Multi-modal Multi-objective Optimization Problem (MMOP), there is a risk that the found approximation sets are not smoothly navigable because the solutions belong to various niches, which reduces the insight for...

An Improved Pareto Front Modeling Algorithm for Large-scale Many-Objective Optimization

A key idea in many-objective optimization is to approximate the optimal Pareto front using a set of representative non-dominated solutions. The produced solution set should be close to the optimal front (convergence) and well-diversified (diversity). Recent studies have shown that measuring both convergence and diversity depends on the shape (or...

Obtaining Smoothly Navigable Approximation Sets in Bi-objective Multi-modal Optimization

Even if a Multi-modal Multi-Objective Evolutionary Algorithm (MMOEA) is designed to find solutions well spread over all locally optimal approximation sets of a Multi-modal Multi-objective Optimization Problem (MMOP), there is a risk that the found set of solutions is not smoothly navigable because the solutions belong to various niches,...

One property to rule them all?: On the limits of trade-offs for S-boxes

Substitution boxes (S-boxes) are nonlinear mappings that represent one of the core parts of many cryptographic algorithms (ciphers). If S-box does not possess good properties, a cipher would be susceptible to attacks. To design suitable S-boxes, we can use heuristics as it allows significant freedom in the selection of required cryptographic...