Antipov, Denis; Kötzing, Timo; Radhakrishnan, Aishwarya **Greedy versus Curious Parent Selection for Multi-Objective Evolutionary Algorithms** 2024

From the literature we know that simple evolutionary multi- objective algorithms can optimize the classic two-objective test functions OneMinMax and CountingOnesCountingZeroes in O(n^2 log n) expected time. We extend this result to any pair of generalized OneMax functions and show that, if the optima of the two functions are d apart, then (G)SEMO has an expected optimization time of O(dn log(n)). In an attempt to achieve better optimization times, some algorithms consider parent selection. We show that parent selection based on the curiosity-based novelty search can improve the optimization time to O(n^2) on OneMinMax. By contrast, we show that greedy parent selection schemes can be trapped with an incomplete Pareto front for superpolynomial time. Finally, we provide experimental results on the two-objective optimization of linear functions.

Harder, Jonathan Gadea; Kötzing, Timo; Li, Xiaoyue; Radhakrishnan, Aishwarya; Ruff, Janosch **Run Time Bounds for Integer-Valued OneMax Functions**Genetic and Evolutionary Computation Conference (GECCO ’24) 2024

Friedrich, Tobias; Kötzing, Timo; Radhakrishnan, Aishwarya; Schiller, Leon; Schirneck, Martin; Tennigkeit, Georg; Wietheger, Simon **Crossover for Cardinality Constrained Optimization**ACM Transactions on Evolutionary Learning and Optimization 2023: 1–32

To understand better how and why crossover can benefit constrained optimization, we consider pseudo-Boolean functions with an upper bound \(B\) on the number of 1-bits allowed in the length-\(n\) bit string (i.e., a cardinality constraint). We investigate the natural translation of the OneMax test function to this setting, a linear function where \(B\) bits have a weight of \(1+ 1/n\) and the remaining bits have a weight of \(1\). Friedrich et al. [TCS 2020] gave a bound of \(\Theta(n^2)\) for the expected running time of the (1+1) EA on this function. Part of the difficulty when optimizing this problem lies in having to improve individuals meeting the cardinality constraint by flipping a \(1\) and a \(0\) simultaneously. The experimental literature proposes balanced operators, preserving the number of 1-bits, as a remedy. We show that a balanced mutation operator optimizes the problem in \(O(n \log n)\) if \(n-B = O(1)\). However, if \(n-B = \Theta(n)\), we show a bound of \(\Omega(n^2)\), just as for classic bit mutation. Crossover together with a simple island model gives running times of \(O(n^2 / \log n)\) (uniform crossover) and \(O(n\sqrt{n})\) (3-ary majority vote crossover). For balanced uniform crossover with Hamming-distance maximization for diversity we show a bound of \(O(n \log n)\). As an additional contribution, we present an extensive analysis of different balanced crossover operators from the literature.

Friedrich, Tobias; Kötzing, Timo; Neumann, Aneta; Neumann, Frank; Radhakrishnan, Aishwarya **Analysis of the (1+1) EA on LeadingOnes with Constraints**Genetic and Evolutionary Computation Conference (GECCO ’23) 2023

Understanding how evolutionary algorithms perform on constrained problems has gained increasing attention in recent years. In this paper, we study how evolutionary algorithms optimize constrained versions of the classical LeadingOnes problem. We first provide a run time analysis for the classical (1+1)~EA on the LeadingOnes problem with a deterministic cardinality constraint, giving \($\Theta(n (n-B)\log(B) + n^2)$\) as the tight bound. Our results show that the behaviour of the algorithm is highly dependent on the constraint bound of the uniform constraint. Afterwards, we consider the problem in the context of stochastic constraints and provide insights using experimental studies on how the (\($\mu$\)+1)~EA is able to deal with these constraints in a sampling-based setting.

Friedrich, Tobias; Kötzing, Timo; Neumann, Frank; Radhakrishnan, Aishwarya **Theoretical Study of Optimizing Rugge Landscapes with the cGA**Parallel Problem Solving From Nature (PPSN) 2022: 586–599

Estimation of distribution algorithms (EDAs) provide a distribution-based approach for optimization which adapts its probability distribution during the run of the algorithm. We contribute to the theoretical understanding of EDAs and point out that their distribution approach makes them more suitable to deal with rugged fitness landscapes than classical local search algorithms. Concretely, we make the OneMax function rugged by adding noise to each fitness value. The cGA can nevertheless find solutions with \(n (1 - \epsilon )\) many 1s, even for high variance of noise. In contrast to this, RLS and the (1+1) EA, with high probability, only find solutions with \(n(1/2 + o(1)) \) many 1s, even for noise with small variance.

Friedrich, Tobias; Kötzing, Timo; Radhakrishnan, Aishwarya; Schiller, Leon; Schirneck, Martin; Tennigkeit, Georg; Wietheger, Simon **Crossover for Cardinality Constrained Optimization**Genetic and Evolutionary Computation Conference (GECCO) 2022: 1399–1407

Best Paper Award (Theory Track)

In order to understand better how and why crossover can benefit optimization, we consider pseudo-Boolean functions with an upper bound \(B\) on the number of 1s allowed in the bit string (cardinality constraint). We consider the natural translation of the OneMax test function, a linear function where \(B\) bits have a weight of \(1+\varepsilon\) and the remaining bits have a weight of \(1\). The literature gives a bound of \(\Theta(n^2)\) for the (1+1) EA on this function. Part of the difficulty when optimizing this problem lies in having to improve individuals meeting the cardinality constraint by flipping both a 1 and a 0. The experimental literature proposes balanced operators, preserving the number of 1s, as a remedy. We show that a balanced mutation operator optimizes the problem in \(O(n \log n)\) if \(n-B = O(1)\). However, if \(n-B = \Theta(n)\), we show a bound of \(\Omega(n^2)\), just as classic bit flip mutation. Crossover and a simple island model gives \(O(n^2 / \log n)\) (uniform crossover) and \(O(n\sqrt{n})\) (3-ary majority vote crossover). For balanced uniform crossover with Hamming distance maximization for diversity we show a bound of \(O(n \log n)\). As an additional contribution we analyze and discuss different balanced crossover operators from the literature.