Papers Evolutionary Algorithms
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Stochastic Population Update Can Provably Be Helpful in Multi-Objective Evolutionary Algorithms
Evolutionary algorithms (EAs) have been widely and successfully applied to solve multi-objective optimization problems, due to their nature of population-based search. Population update, a key component in multi-objectiv…
Evolutionary AlgorithmsContinuous Cartesian Genetic Programming based representation for Multi-Objective Neural Architecture Search
We propose a novel approach for the challenge of designing less complex yet highly effective convolutional neural networks (CNNs) through the use of cartesian genetic programming (CGP) for neural architecture search (NAS…
Evolutionary AlgorithmsNeural Architecture SearchRepresentation-agnostic distance-driven perturbation for optimizing ill-conditioned problems
Locality is a crucial property for efficiently optimising black-box problems with randomized search heuristics. However, in practical applications, it is not likely to always find such a genotype encoding of candidate so…
Evolutionary AlgorithmsEfficient automatic design of robots
Robots are notoriously difficult to design because of complex interdependencies between their physical structure, sensory and motor layouts, and behavior. Despite this, almost every detail of every robot built to date ha…
Evolutionary AlgorithmsBayesian Decision Trees Inspired from Evolutionary Algorithms
Bayesian Decision Trees (DTs) are generally considered a more advanced and accurate model than a regular Decision Tree (DT) because they can handle complex and uncertain data. Existing work on Bayesian DTs uses Markov Ch…
Evolutionary AlgorithmsOn the Impact of Operators and Populations within Evolutionary Algorithms for the Dynamic Weighted Traveling Salesperson Problem
Evolutionary algorithms have been shown to obtain good solutions for complex optimization problems in static and dynamic environments. It is important to understand the behaviour of evolutionary algorithms for complex op…
Evolutionary AlgorithmsA Fitness-assignment Method for Evolutionary Constrained Multi-objective Optimization
The effectiveness of Constrained Multi-Objective Evolutionary Algorithms (CMOEAs) depends on their ability to reach the different feasible regions during evolution, by exploiting the information present in infeasible sol…
Density EstimationEvolutionary AlgorithmsAnalysis of the (1+1) EA on LeadingOnes with Constraints
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 classica…
Evolutionary AlgorithmsNon-Elitist Evolutionary Multi-Objective Optimisation: Proof-of-Principle Results
Elitism, which constructs the new population by preserving best solutions out of the old population and newly-generated solutions, has been a default way for population update since its introduction into multi-objective …
DiversityEvolutionary AlgorithmsVector Autoregressive Evolution for Dynamic Multi-Objective Optimisation
Dynamic multi-objective optimisation (DMO) handles optimisation problems with multiple (often conflicting) objectives in varying environments. Such problems pose various challenges to evolutionary algorithms, which have …
DiversityEvolutionary AlgorithmsThe First Proven Performance Guarantees for the Non-Dominated Sorting Genetic Algorithm II (NSGA-II) on a Combinatorial Optimization Problem
The Non-dominated Sorting Genetic Algorithm-II (NSGA-II) is one of the most prominent algorithms to solve multi-objective optimization problems. Recently, the first mathematical runtime guarantees have been obtained for …
Combinatorial OptimizationEvolutionary AlgorithmsEvolutionary Algorithms in the Light of SGD: Limit Equivalence, Minima Flatness, and Transfer Learning
Whenever applicable, the Stochastic Gradient Descent (SGD) has shown itself to be unreasonably effective. Instead of underperforming and getting trapped in local minima due to the batch noise, SGD leverages it to learn t…
Evolutionary AlgorithmsTransfer LearningEvolutionary Diversity Optimisation in Constructing Satisfying Assignments
Computing diverse solutions for a given problem, in particular evolutionary diversity optimisation (EDO), is a hot research topic in the evolutionary computation community. This paper studies the Boolean satisfiability p…
DiversityEvolutionary AlgorithmsRuntime Analyses of Multi-Objective Evolutionary Algorithms in the Presence of Noise
In single-objective optimization, it is well known that evolutionary algorithms also without further adjustments can tolerate a certain amount of noise in the evaluation of the objective function. In contrast, this quest…
Evolutionary AlgorithmsLimit-behavior of a hybrid evolutionary algorithm for the Hasofer-Lind reliability index problem
In probabilistic structural mechanics, the Hasofer-Lind reliability index problem is a paradigmatic equality constrained problem of searching for the minimum distance from a point to a surface. In practical engineering p…
Evolutionary AlgorithmsAccelerating genetic optimization of nonlinear model predictive control by learning optimal search space size
Genetic algorithm (GA) is typically used to solve nonlinear model predictive control's optimization problem. However, the size of the search space in which the GA searches for the optimal control inputs is crucial for it…
Evolutionary AlgorithmsModel Predictive ControlMulti-Value Alignment in Normative Multi-Agent System: Evolutionary Optimisation Approach
Value-alignment in normative multi-agent systems is used to promote a certain value and to ensure the consistent behavior of agents in autonomous intelligent systems with human values. However, the current literature is …
Evolutionary AlgorithmsFast Pareto Optimization Using Sliding Window Selection
Pareto optimization using evolutionary multi-objective algorithms has been widely applied to solve constrained submodular optimization problems. A crucial factor determining the runtime of the used evolutionary algorithm…
Evolutionary AlgorithmsTheoretical Analyses of Evolutionary Algorithms on Time-Linkage OneMax with General Weights
Evolutionary computation has shown its superiority in dynamic optimization, but for the (dynamic) time-linkage problems, some theoretical studies have revealed the possible weakness of evolutionary computation. Since the…
Evolutionary AlgorithmsLarger Offspring Populations Help the $(1 + (λ, λ))$ Genetic Algorithm to Overcome the Noise
Evolutionary algorithms are known to be robust to noise in the evaluation of the fitness. In particular, larger offspring population sizes often lead to strong robustness. We analyze to what extent the $(1+(\lambda,\lamb…
Evolutionary Algorithms