70 citations · 95 across the 12 of their papers we have counts for
15 papers
Runtime Analysis of Cartesian Genetic Programming in Evolving Boolean Functions
Duc-Cuong Dang, Roman Kalkreuth, Andre Opris
Cartesian Genetic Programming (CGP) is among the practical and popular forms of Genetic Programming as it uses a graph-based representation of programs. This paper presents a first…
SPEA2: Improved Density Estimation in SPEA2 with Provable Runtime Guarantees
Duc-Cuong Dang, Andre Opris, Dirk Sudholt
The Strength Pareto Evolutionary Algorithm 2 (SPEA2) is a popular and prominent evolutionary algorithm for solving multi-objective optimisation problems. Despite its popularity, th…
Theoretical Analysis of Quality Diversity Algorithms for a Classical Path Planning Problem
Duc-Cuong Dang, Aneta Neumann, Frank Neumann +2
Quality diversity (QD) algorithms have shown to provide sets of high quality solutions for challenging problems in robotics, games, and combinatorial optimisation. So far, theoreti…
Illustrating the Efficiency of Popular Evolutionary Multi-Objective Algorithms Using Runtime Analysis
Duc-Cuong Dang, Andre Opris, Dirk Sudholt
Runtime analysis has recently been applied to popular evolutionary multi-objective (EMO) algorithms like NSGA-II in order to establish a rigorous theoretical foundation. However, m…
Runtime Analyses of NSGA-III on Many-Objective Problems
Andre Opris, Duc-Cuong Dang, Frank Neumann +1
NSGA-II and NSGA-III are two of the most popular evolutionary multi-objective algorithms used in practice. While NSGA-II is used for few objectives such as 2 and 3, NSGA-III is des…
Analysing the Robustness of NSGA-II under Noise
Duc-Cuong Dang, Andre Opris, Bahare Salehi +1
Runtime analysis has produced many results on the efficiency of simple evolutionary algorithms like the (1+1) EA, and its analogue called GSEMO in evolutionary multiobjective optim…
Crossover Can Guarantee Exponential Speed-Ups in Evolutionary Multi-Objective Optimisation
Duc-Cuong Dang, Andre Opris, Dirk Sudholt
Evolutionary algorithms are popular algorithms for multiobjective optimisation (also called Pareto optimisation) as they use a population to store trade-offs between different obje…
Runtime Analysis of Fitness-Proportionate Selection on Linear Functions
Duc-Cuong Dang, Anton Eremeev, Per Kristian Lehre
This paper extends the runtime analysis of non-elitist evolutionary algorithms (EAs) with fitness-proportionate selection from the simple OneMax function to the linear functions. N…
Level-Based Analysis of the Univariate Marginal Distribution Algorithm
Duc-Cuong Dang, Per Kristian Lehre, Phan Trung Hai Nguyen
Estimation of Distribution Algorithms (EDAs) are stochastic heuristics that search for optimal solutions by learning and sampling from probabilistic models. Despite their popularit…
Escaping Local Optima using Crossover with Emergent or Reinforced Diversity
Duc-Cuong Dang, Tobias Friedrich, Timo Kötzing +5
Population diversity is essential for avoiding premature convergence in Genetic Algorithms (GAs) and for the effective use of crossover. Yet the dynamics of how diversity emerges i…
Populations can be essential in tracking dynamic optima
Duc-Cuong Dang, Thomas Jansen, Per Kristian Lehre
Real-world optimisation problems are often dynamic. Previously good solutions must be updated or replaced due to changes in objectives and constraints. It is often claimed that evo…
Self-adaptation of Mutation Rates in Non-elitist Populations
Duc-Cuong Dang, Per Kristian Lehre
The runtime of evolutionary algorithms (EAs) depends critically on their parameter settings, which are often problem-specific. Automated schemes for parameter tuning have been deve…
Solving the Team Orienteering Problem with Cutting Planes
Racha El-Hajj, Duc-Cuong Dang, Aziz Moukrim
The Team Orienteering Problem (TOP) is an attractive variant of the Vehicle Routing Problem (VRP). The aim is to select customers and at the same time organize the visits for a veh…
Level-Based Analysis of Genetic Algorithms for Combinatorial Optimization
Duc-Cuong Dang, Anton V. Eremeev, Per Kristian Lehre
The paper is devoted to upper bounds on run-time of Non-Elitist Genetic Algorithms until some target subset of solutions is visited for the first time. In particular, we consider t…
Level-based Analysis of Genetic Algorithms and other Search Processes
Dogan Corus, Duc-Cuong Dang, Anton V. Eremeev +1
Understanding how the time-complexity of evolutionary algorithms (EAs) depend on their parameter settings and characteristics of fitness landscapes is a fundamental problem in evol…