Program-adaptive mutational fuzzing
We present the design of an algorithm to maximize the number of bugs found for black-box
mutational fuzzing given a program and a seed input. The major intuition is to leverage white …
mutational fuzzing given a program and a seed input. The major intuition is to leverage white …
Hyper-heuristics using multi-armed bandit models for multi-objective optimization
In this work, we explore different multi-armed bandit-based hyper-heuristics applied to the
multi-objective permutation flow shop problem. It is a scheduling problem which has been …
multi-objective permutation flow shop problem. It is a scheduling problem which has been …
Decomposition multi-objective evolutionary optimization: From state-of-the-art to future opportunities
K Li - arXiv preprint arXiv:2108.09588, 2021 - arxiv.org
Decomposition has been the mainstream approach in the classic mathematical
programming for multi-objective optimization and multi-criterion decision-making. However …
programming for multi-objective optimization and multi-criterion decision-making. However …
Dynamic multiobjective evolutionary algorithm with adaptive response mechanism selection strategy
L Chen, H Wang, D Pan, H Wang, W Gan… - Knowledge-Based …, 2022 - Elsevier
In this paper, a dynamic multiobjective evolutionary algorithm (DMOEA) with an adaptive
response mechanism selection strategy is proposed to address the shortcoming that a …
response mechanism selection strategy is proposed to address the shortcoming that a …
Multi-armed bandit-based hyper-heuristics for combinatorial optimization problems
There are significant research opportunities in the integration of Machine Learning (ML)
methods and Combinatorial Optimization Problems (COPs). In this work, we focus on …
methods and Combinatorial Optimization Problems (COPs). In this work, we focus on …
An autoselection strategy of multiobjective evolutionary algorithms based on performance indicator and its application
Q Fan, Y Zhang, N Li - IEEE Transactions on Automation …, 2021 - ieeexplore.ieee.org
The use of ensemble approaches in the single-objective evolutionary algorithms is
ubiquitous, but ensembles of multiobjective evolutionary algorithms (MOEAs) have achieved …
ubiquitous, but ensembles of multiobjective evolutionary algorithms (MOEAs) have achieved …
Solving multi-objective optimization problem using cuckoo search algorithm based on decomposition
L Chen, W Gan, H Li, K Cheng, D Pan, L Chen… - Applied …, 2021 - Springer
In recent years, cuckoo search (CS) algorithm has been successfully applied in single-
objective optimization problems. In addition, decomposition-based multi-objective …
objective optimization problems. In addition, decomposition-based multi-objective …
Cross-domain algorithm selection: Algorithm selection across selection hyper-heuristics
M Misir - 2022 IEEE Symposium Series on Computational …, 2022 - ieeexplore.ieee.org
The present study introduces algorithm selection on selection hyper-heuristics. Hyper-
heuristics are known as problem-independent methods utilized to solve different instances …
heuristics are known as problem-independent methods utilized to solve different instances …
On the use of contextual information for machine learning based test case prioritization in continuous integration development
EA da Roza, JA do Prado Lima, SR Vergilio - Information and Software …, 2024 - Elsevier
Context: In most software organizations, Continuous Integration (CI) is a common practice
usually subject to some budgets. Consequently, prioritizing test cases to be executed in the …
usually subject to some budgets. Consequently, prioritizing test cases to be executed in the …
A cricket-based selection hyper-heuristic for many-objective optimization problems
While meta-heuristics are usually designed for the optimization problems of the same
domain and can achieve superior performance compared with heuristics, their performances …
domain and can achieve superior performance compared with heuristics, their performances …