Soaring migrant birds exploit columns of rising air (thermals) to cover large distances with minimal energy. Using social information while locating thermals may benefit such birds, but examining collective movements in wild migrants has been a major challenge for researchers. We investigated the group movements of a flock of 27 naturally migrating juvenile white storks by using high-resolution GPS and accelerometers. Analyzing individual and group movements on multiple scales revealed that a small number of leaders navigated to and explored thermals, whereas followers benefited from their movements. Despite this benefit, followers often left thermals earlier and at lower height, and consequently they had to flap considerably more.

Extended Predictable Feature Analysis (PFAx) [Richthofer and Wiskott, 2017] is an extension of PFA [Richthofer and Wiskott, 2015] that allows generating a goal-directed control signal of an agent whose dynamics has previously been learned during a training phase in an unsupervised manner. PFAx hardly requires assumptions or prior knowledge of the agent's sensor or control mechanics, or of the environment. It selects features from a high-dimensional input by intrinsic predictability and organizes them into a reasonably low-dimensional model. While PFA obtains a well predictable model, PFAx yields a model ideally suited for manipulations with predictable outcome. This allows for goal-directed manipulation of an agent and thus for local navigation, i.e. for reaching states where intermediate actions can be chosen by a permanent descent of distance to the goal. The approach is limited when it comes to global navigation, e.g. involving obstacles or multiple rooms. In this article, we extend theoretical results from [Sprekeler and Wiskott, 2008], enabling PFAx to perform stable global navigation. So far, the most widely exploited characteristic of Slow Feature Analysis (SFA) was that slowness yields invariances. We focus on another fundamental characteristics of slow signals: They tend to yield monotonicity and one significant property of monotonicity is that local optimization is sufficient to find a global optimum. We present an SFA-based algorithm that structures an environment such that navigation tasks hierarchically decompose into subgoals. Each of these can be efficiently achieved by PFAx, yielding an overall global solution of the task. The algorithm needs to explore and process an environment only once and can then perform all sorts of navigation tasks efficiently. We support this algorithm by mathematical theory and apply it to different problems.

This course will guide you on what optimization is and what metaheuristics are. You will learn why we use metaheuristics in optimization problems as sometimes, when you have a complex problem you'd like to optimize, deterministic methods will not do; you will not be able to reach the best and optimal solution to your problem, therefore, metaheuristics should be used. This course covers information on metaheuristics and four widely used techniques which are Simulated Annealing, Genetic Algorithm, Tabu Search, and Evolutionary Strategies. By the end of this course, you will learn what Simulated Annealing, Genetic Algorithm, Tabu Search, and Evolutionary Strategies are, why they are used, how they work, and best of all, how to code them in Python! You will also learn how to handle constraints.

This book brings together - in an informal and tutorial fashion - the computer techniques, mathematical tools, and research results that will enable both students and practitioners to apply genetic algorithms to problems in many fields. Major concepts are illustrated with running examples, and major algorithms are illustrated by Pascal computer programs. No prior knowledge of GAs or genetics is assumed, and only a minimum of computer programming and mathematics background is required.

This is the MATLAB code for the K-RVEA algorithm published in the following article: T. Chugh, Y. Jin, K. Miettinen, J. Hakanen, and K. Sindhya, A surrogate-assisted reference vector guided evolutionary algorithm for computationally expensive many-objective optimization, IEEE Transactions on Evolutionary Computation, vol. More details about it can be found in the thesis: T. Chugh. Please read the licence file before using the code and cite the article and the thesis if you use the code.

In this paper, we present a parallel genetic algorithm (pGA) with adaptive control parameters and permutation representation for weighted tardiness scheduling with sequence-dependent setups, an NP-Hard problem. This pGA provides a linear to slightly superlinear speedup relative to its sequential counterpart. As part of our research, we explore the effects of different random number generation algorithms on the runtimes of both sequential and parallel GAs. GAs and other forms of evolutionary computation rely so heavily on random number generation that our results show that we can obtain a 20% increase in the speed of a pGA, and an over 25% increase in the speed of a sequential GA, simply by careful choice of random number generator---both the underlying generator as well as algorithms for specific number types such as Gaussian often needed for mutating real-valued genes.

In this work, we describe a self-replication-based mechanism for designing agents of increasing complexity. We demonstrate the validity of this approach by solving simple, standard evolutionary computation problems in simulation. In the context of these simulation results, we describe the fundamental differences of this approach when compared to traditional approaches. Further, we highlight the possible advantages of applying this approach to the problem of designing complex artificial agents, along with the potential drawbacks and issues to be addressed in the future.

To investigate the consequences of hybridization between species, we studied three replicate hybrid populations that formed naturally between two swordtail fish species, estimating their fine-scale genetic map and inferring ancestry along the genomes of 690 individuals. In all three populations, ancestry from the "minor" parental species is more common in regions of high recombination and where there is linkage to fewer putative targets of selection. The same patterns are apparent in a reanalysis of human and archaic admixture. These results support models in which ancestry from the minor parental species is more likely to persist when rapidly uncoupled from alleles that are deleterious in hybrids. Our analyses further indicate that selection on swordtail hybrids stems predominantly from deleterious combinations of epistatically interacting alleles.

When bees leave their hive hoping to find a better location for their nest, they often first settle nearby, usually on a tree branch, and cluster around the queen while several dozen scouts go off in search of a new home. Each scout then returns and starts to dance, indicating the direction and distance of the site it found. The more excited they become, the more frantically they dance, signaling the others to have a look. Ultimately, a favorite location emerges from all this swarming and buzzing about -- and they all depart and fly to it. In computer science, this behavior is known as particle swarm optimization, which holds that each particle's movement not only is influenced by its own position but is guided to other good positions, all of which are updated as other particles find better positions.

Sudoku is a well-known logic-based puzzle game that was first published in 1979 under the name of "Number Place". It was popularised in Japan in 1984 by the puzzle company Nikoli, and later named "Sudoku", which roughly translates to "single digits". The puzzle gained attention in the West in 2004, after The Times published its first Sudoku grid (at the instigation of Hong Kong-based judge Wayne Gould, who first encountered the puzzle in 1997, and developed a computer program to automatically generate instances). Sudoku is now a global phenomenon, and many newspapers now carry it alongside their existing crosswords (see [4] for a general history of the puzzle). The simplest variant of Sudoku uses a 9 9 grid of cells divided into nine 3 3 subgrids (Figure 1 (left)). The aim of the puzzle is to fill the grid with digits such that each row, each column, and each 3 3 subgrid contains all of the digits 1-9 (Figure 1 (right)). An instance of Sudoku provides, at the outset, a partially-completed grid, but the difficulty of any grid derives more from the range of techniques required to solve it than the number of cell values that are provided for the player. Sudoku is an NPcomplete problem [12], as first shown in [35] (via a reduction from the Latin Square Completion problem [2]).