6 citations · 14 across the 18 of their papers we have counts for
16 papers · 1 filter
Provable Speedups From Dynamic Population Sizes in Evolutionary Algorithms for Multiobjective Optimization
Andre Opris
This paper investigates the role of dynamic population sizes in evolutionary multi-objective optimization. Although such approaches are widely used in practice, their benefits rema…
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…
On the Impact of Crossover in Many-Objective Optimization: A Runtime Analysis of NSGA-III
Andre Opris
In recent years, a theoretical understanding has rapidly advanced regarding how popular multi-objective evolutionary algorithms (MOEAs) can optimize many-objective problems. Howeve…
Parent Selection Mechanisms in Elitist Crossover-Based Algorithms
Andre Opris, Denis Antipov
Parent selection methods are widely used in evolutionary computation to accelerate the optimization process, yet their theoretical benefits are still poorly understood. In this pap…
Towards a Rigorous Understanding of the Population Dynamics of the NSGA-III: Tight Runtime Bounds
Andre Opris
Evolutionary algorithms are widely used for solving multi-objective optimization problems. A prominent example is NSGA-III, which is particularly well suited for solving problems i…
A First Runtime Analysis of the PAES-25: An Enhanced Variant of the Pareto Archived Evolution Strategy
Andre Opris
This paper presents a first mathematical runtime analysis of PAES-25, an enhanced version of the original Pareto Archived Evolution Strategy (PAES) coming from the study of telecom…
Tight Runtime Guarantees From Understanding the Population Dynamics of the GSEMO Multi-Objective Evolutionary Algorithm
Benjamin Doerr, Martin Krejca, Andre Opris
The global simple evolutionary multi-objective optimizer (GSEMO) is a simple, yet often effective multi-objective evolutionary algorithm (MOEA). By only maintaining non-dominated s…
Runtime Analyses of NSGA-III on Many-Objective Problems: Provable Exponential Speedup via Stochastic Population Update
Andre Opris
NSGA-III is a prominent algorithm in evolutionary many-objective optimization. It is particularly well suited for optimizing problems with more than three objectives, distinguishin…
Many Objective Problems Where Crossover is Provably Essential
Andre Opris
This article addresses theory in evolutionary many-objective optimization and focuses on the role of crossover operators. The advantages of using crossover are hardly understood an…
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…
Achieving Tight Runtime Bounds on Jump by Proving that Genetic Algorithms Evolve Near-Maximal Population Diversity
Andre Opris, Johannes Lengler, Dirk Sudholt
The JUMP benchmark was the first problem for which crossover was proven to give a speed-up over mutation-only evolutionary algorithms. Jansen and Wegener (2002) proved an upper…
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…
Analysing Equilibrium States for Population Diversity
Johannes Lengler, Andre Opris, Dirk Sudholt
Population diversity is crucial in evolutionary algorithms as it helps with global exploration and facilitates the use of crossover. Despite many runtime analyses showing advantage…
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…