Genetic programming (another name for evolutionary systems) creates generations of computer programs "using the principles of Darwinian natural selection and biologically inspired operations. The operations include reproduction, crossover (sexual recombination), mutation, and architecture-altering operations patterned after gene duplication and gene deletion in nature."
– Genetic Programming, Inc.
GANs are formulated as a game between two networks that compete to reach Nash equilibrium. There's a discriminator, D, which attempts to distinguish between real and fake data, and a generator, G, that attempts to trick D. Data is labeled as synthetic or real, D is trained to minimize the NLL of the labeled data, and G is trained to maximize it. It seems to me that, given infinite capacity, D could simply learn that every single example that isn't the real data is synthetic; G would then learn to simply reproduce the real data verbatim, more likely settling on a single example (mode collapse) since constant functions are easy to learn; promoting D to a distribution, Nash equilibrium would then be reached by D assigning 1/2 to each singular data point G could replicate. But this evidently doesn't happen, at least not all the time. I'm a bit perplexed by why it doesn't play out like this in most papers (I've personally encountered it plenty, though).
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.
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.
Perceptual generalization and discrimination are fundamental cognitive abilities. For example, if a bird eats a poisonous butterfly, it will learn to avoid preying on that species again by generalizing its past experience to new perceptual stimuli. In cognitive science, the "universal law of generalization" seeks to explain this ability and states that generalization between stimuli will follow an exponential function of their distance in "psychological space." Here, I challenge existing theoretical explanations for the universal law and offer an alternative account based on the principle of efficient coding. I show that the universal law emerges inevitably from any information processing system (whether biological or artificial) that minimizes the cost of perceptual error subject to constraints on the ability to process or transmit information.
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.
Can't Stop is a jeopardy stochastic game played on an octagonal game board with four six-sided dice. Optimal strategies have been computed for some simplified versions of Can't Stop by employing retrograde analysis and value iteration combined with Newton's method. These computations result in databases that map game positions to optimal moves. Solving the original game, however, is infeasible with current techniques and technology. This paper describes the creation of heuristic strategies for solitaire Can't Stop by generalizing an existing heuristic and using genetic algorithms to optimize the generalized parameters. The resulting heuristics are easy to use and outperform the original heuristic by 19%. Results of the genetic algorithm are compared to the known optimal results for smaller versions of Can't Stop, and data is presented showing the relative insensitivity of the particular genetic algorithm used to the balance between reduced noise and increased population diversity.
We use genetic programming to evolve highly successful solvers for two puzzles: Rush Hour and FreeCell. Many NP-Complete puzzles have remained relatively neglected by researchers (see (Kendall, Parkes, and Spoerer 2008) for a review). Among these difficult games we find the Rush Hour puzzle, which was proven to be PSPACE-Complete for the general n n case (Flake and Baum 2002). The commercial version of this popular single-player game is played on a 6x6 grid, simulating a parking lot replete with several cars and trucks. The goal is to find a sequence of legal vehicular moves that ultimately clears the way for the red target car, allowing it to exit the lot through a tile that marks the exit (Figure 1a).