Identity by Descent: Variation in Meiosis, Across

... In Inference of Relationships, Relatedness, and IBD Segments we consider the estimation of relationships and relatedness and the inference of IBD in individuals not known to be related. We do not cover estimation of admixture (McKeigue 1998) or inference of hybrids (Anderson and Thompson 2002) or th ...

... In Inference of Relationships, Relatedness, and IBD Segments we consider the estimation of relationships and relatedness and the inference of IBD in individuals not known to be related. We do not cover estimation of admixture (McKeigue 1998) or inference of hybrids (Anderson and Thompson 2002) or th ...

Limitations of Front-to-End Bidirectional Heuristic Search

... If a unidirectional heuristic search expands the majority of its nodes deeper than the solution midpoint, we call its heuristic weak and show that a bidirectional heuristic search expands no fewer nodes than a bidirectional bruteforce search. If the majority are expanded at shallower depth than the ...

... If a unidirectional heuristic search expands the majority of its nodes deeper than the solution midpoint, we call its heuristic weak and show that a bidirectional heuristic search expands no fewer nodes than a bidirectional bruteforce search. If the majority are expanded at shallower depth than the ...

artificial intelligence (luger, 6th, 2008)

... Intelligence is too complex to be described by any single theory; instead, researchers are constructing a hierarchy of theories that characterize it at multiple levels of abstraction. At the lowest levels of this hierarchy, neural networks, genetic algorithms and other forms of emergent computation ...

... Intelligence is too complex to be described by any single theory; instead, researchers are constructing a hierarchy of theories that characterize it at multiple levels of abstraction. At the lowest levels of this hierarchy, neural networks, genetic algorithms and other forms of emergent computation ...

Symbolic Analysis of Large Analog Integrated Circuits

... The same deletion/contraction operation is repeated for the next element with smallest contribution, and so on. The reduction in formula complexity is more significant for larger circuits. Reported approaches evaluate the influence of the elimination of matrix entries [15], [16] or graph branches [7 ...

... The same deletion/contraction operation is repeated for the next element with smallest contribution, and so on. The reduction in formula complexity is more significant for larger circuits. Reported approaches evaluate the influence of the elimination of matrix entries [15], [16] or graph branches [7 ...

Bayesian AI Introduction - Australasian Bayesian Network Modelling

... Bayesian networks are the basis for a new generation of probabilistic expert systems, which allow for exact (and approximate) modelling of physical, biological and social systems operating under uncertainty. Bayesian networks are also an important representational tool for data mining, in causal dis ...

... Bayesian networks are the basis for a new generation of probabilistic expert systems, which allow for exact (and approximate) modelling of physical, biological and social systems operating under uncertainty. Bayesian networks are also an important representational tool for data mining, in causal dis ...

Differential Equations

... The purpose of these notes is to organize a large part of the theory for this course. Your text has much additional discussion about how to calculate given problems and, more importantly, the detailed analysis of many applied problems. I include some applications in these notes, but my focus is much ...

... The purpose of these notes is to organize a large part of the theory for this course. Your text has much additional discussion about how to calculate given problems and, more importantly, the detailed analysis of many applied problems. I include some applications in these notes, but my focus is much ...

Planning with Markov Decision Processes

... field, from the basics to state-of-the-art optimal and approximation algorithms. We first describe the theoretical foundations of MDPs and the fundamental solution techniques for them. We then discuss modern optimal algorithms based on heuristic search and the use of structured representations. A ma ...

... field, from the basics to state-of-the-art optimal and approximation algorithms. We first describe the theoretical foundations of MDPs and the fundamental solution techniques for them. We then discuss modern optimal algorithms based on heuristic search and the use of structured representations. A ma ...

Landmarks Revisited Silvia Richter Malte Helmert

... searching for the original goal of the planning task. For this purpose, it is desirable to be able to smoothly integrate the landmark information with other useful heuristics. The most straightforward way of using landmark information for search is to estimate the goal distance of a state s by the n ...

... searching for the original goal of the planning task. For this purpose, it is desirable to be able to smoothly integrate the landmark information with other useful heuristics. The most straightforward way of using landmark information for search is to estimate the goal distance of a state s by the n ...

Joint Stocking and Sourcing Policies for a Single–Depot, Single

... Our focus is on a one-base one-depot problem for three main reasons: 1) such environments are realistic in many cases as we discuss below; 2) it allows us to develop structural results and managerial insights, which are more difficult to generate in multiple-base environments and, 3) our analysis o ...

... Our focus is on a one-base one-depot problem for three main reasons: 1) such environments are realistic in many cases as we discuss below; 2) it allows us to develop structural results and managerial insights, which are more difficult to generate in multiple-base environments and, 3) our analysis o ...

ppt - Dave Reed

... if constructing and searching at the same time, then it depends • if many insertions, followed by searches, use merge sort – do all insertions O(N), then sort O(N log N), then searches O(log N) • if insertions and searches are mixed, then insertion sort – each insertion is O(N) as opposed to O(N l ...

... if constructing and searching at the same time, then it depends • if many insertions, followed by searches, use merge sort – do all insertions O(N), then sort O(N log N), then searches O(log N) • if insertions and searches are mixed, then insertion sort – each insertion is O(N) as opposed to O(N l ...

pdf file - The Department of Computer Science

... Relaxation is a predominant approach to simplifying planning problems. Solutions of the relaxed planning problem can be used to guide search in the original planning task. The forward propagation heuristic hadd (Bonet, Loerincs, and Geffner 1997; Bonet and Geffner 2001) was used in the heuristic sea ...

... Relaxation is a predominant approach to simplifying planning problems. Solutions of the relaxed planning problem can be used to guide search in the original planning task. The forward propagation heuristic hadd (Bonet, Loerincs, and Geffner 1997; Bonet and Geffner 2001) was used in the heuristic sea ...

# Genetic algorithm

In the field of artificial intelligence, a genetic algorithm (GA) is a search heuristic that mimics the process of natural selection. This heuristic (also sometimes called a metaheuristic) is routinely used to generate useful solutions to optimization and search problems. Genetic algorithms belong to the larger class of evolutionary algorithms (EA), which generate solutions to optimization problems using techniques inspired by natural evolution, such as inheritance, mutation, selection, and crossover.