In this paper, we introduce evolutionary diversity optimisation (EDO) approaches for the TSP that find a diverse set of tours when the optimal tour is known or unknown.
Computing diverse sets of high-quality solutions has gained increasing attention among the evolutionary computation community in recent years.
In practise, it is often desirable to provide the decision-maker with a rich set of diverse solutions of decent quality instead of just a single solution.
Besides, we introduce a multi-component fitness function for solving the large-scale stockpile blending problem which can maximize the volume of metal over the plan and maintain the balance between stockpiles according to the usage of metal.
Generating diverse populations of high quality solutions has gained interest as a promising extension to the traditional optimization tasks.
We perform runtime analysis of a randomized search algorithm (RSA) and a basic evolutionary algorithm (EA) for the chance-constrained knapsack problem with correlated uniform weights.
In this paper, we consider the uncertainty in material grades and introduce chance constraints that are used to ensure the constraints with high confidence.
We show that the GSEMO algorithm obtains the same worst case performance guarantees as recently analyzed greedy algorithms.
The Traveling Salesperson Problem (TSP) is one of the best-known combinatorial optimisation problems.
Evolving diverse sets of high quality solutions has gained increasing interest in the evolutionary computation literature in recent years.
We use this model in combination with the problem-specific crossover operator in multi-objective evolutionary algorithms to solve the problem.
We introduce an evolutionary image painting approach whose underlying biased random walk can be controlled by a parameter influencing the bias of the random walk and thereby creating different artistic painting effects.
In this paper, we consider the dynamic chance-constrained knapsack problem where the weight of each item is stochastic, the capacity constraint changes dynamically over time, and the objective is to maximize the total profit subject to the probability that total weight exceeds the capacity.
We study three different decision tasks: classic one-shot optimization (only the best sample matters), one-shot optimization with surrogates (allowing to use surrogate models for selecting a design that need not necessarily be one of the evaluated samples), and one-shot regression (i. e., function approximation, with minimization of mean squared error as objective).
We show that our proposed algorithm competes with the state-of-the-art in static settings.
In the experiment section, we evaluate and compare the deterministic approaches and evolutionary algorithms on a wide range of instances.
Evolutionary diversity optimization aims to compute a diverse set of solutions where all solutions meet a given quality criterion.
We also consider EAMC, a new evolutionary algorithm with polynomial expected time guarantee to maintain $\phi$ approximation ratio, and NSGA-II with two different population sizes as advanced multi-objective optimization algorithm, to demonstrate their challenges in optimizing the maximum coverage problem.
To date this work has focused on the generation of images concordant with one or more classes and transfer of artistic styles.
This approach is very flexible in that it can work with a wide range of features and enables targeting specific regions in the images.