4 Learning, Regret Minimization, and Equilibria A
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An Iterated Greedy Metaheuristic for the Blocking Job Shop Scheduling Problem
Dipartimento di Ingegneria Via della Vasca Navale, 79 00146 Roma, Italy An Iterated Greedy Metaheuristic for the Blocking Job Shop Scheduling Problem Marco Pranzo1, Dario Pacciarelli2 RT-DIA-208-2013 Novembre 2013 (1) Universit`adegli Studi Roma Tre, Via della Vasca Navale, 79 00146 Roma, Italy (2) Universit`adegli Studi di Siena Via Roma 56 53100 Siena, Italy. ABSTRACT In this paper we consider a job shop scheduling problem with blocking (zero buffer) con- straints. Blocking constraints model the absence of buffers, whereas in the traditional job shop scheduling model buffers have infinite capacity. There are two known variants of this problem, namely the blocking job shop scheduling (BJSS) with swap allowed and the BJSS with no-swap. A swap is needed when there is a cycle of two or more jobs each waiting for the machine occupied by the next job in the cycle. Such a cycle represents a deadlock in no-swap BJSS, while with a swap all the jobs in the cycle move simulta- neously to their subsequent machine. This scheduling problem is receiving an increasing interest in the recent literature, and we propose an Iterated Greedy (IG) algorithm to solve both variants of the problem. The IG is a metaheuristic based on the repetition of a destruction phase, which removes part of the solution, and a construction phase, in which a new solution is obtained by applying an underlying greedy algorithm starting from the partial solution. Comparison with recent published results shows that the iterated greedy outperforms other state-of-the-art algorithms on benchmark instances. -
GREEDY ALGORITHMS Greedy Algorithms Solve Problems by Making the Choice That Seems Best at the Particular Moment
GREEDY ALGORITHMS Greedy algorithms solve problems by making the choice that seems best at the particular moment. Many optimization problems can be solved using a greedy algorithm. Some problems have no efficient solution, but a greedy algorithm may provide a solution that is close to optimal. A greedy algorithm works if a problem exhibits the following two properties: 1. Greedy choice property: A globally optimal solution can be arrived at by making a locally optimal solution. In other words, an optimal solution can be obtained by making "greedy" choices. 2. optimal substructure. Optimal solutions contain optimal sub solutions. In other Words, solutions to sub problems of an optimal solution are optimal. Difference Between Greedy and Dynamic Programming The most difference between greedy algorithms and dynamic programming is that we don’t solve every optimal sub-problem with greedy algorithms. In some cases, greedy algorithms can be used to produce sub-optimal solutions. That is, solutions which aren't necessarily optimal, but are perhaps very dose. In dynamic programming, we make a choice at each step, but the choice may depend on the solutions to subproblems. In a greedy algorithm, we make whatever choice seems best at 'the moment and then solve the sub-problems arising after the choice is made. The choice made by a greedy algorithm may depend on choices so far, but it cannot depend on any future choices or on the solutions to sub- problems. Thus, unlike dynamic programming, which solves the sub-problems bottom up, a greedy strategy usually progresses in a top-down fashion, making one greedy choice after another, interactively reducing each given problem instance to a smaller one. -
Greedy Estimation of Distributed Algorithm to Solve Bounded Knapsack Problem
Shweta Gupta et al, / (IJCSIT) International Journal of Computer Science and Information Technologies, Vol. 5 (3) , 2014, 4313-4316 Greedy Estimation of Distributed Algorithm to Solve Bounded knapsack Problem Shweta Gupta#1, Devesh Batra#2, Pragya Verma#3 #1Indian Institute of Technology (IIT) Roorkee, India #2Stanford University CA-94305, USA #3IEEE, USA Abstract— This paper develops a new approach to find called probabilistic model-building genetic algorithms, solution to the Bounded Knapsack problem (BKP).The are stochastic optimization methods that guide the search Knapsack problem is a combinatorial optimization problem for the optimum by building probabilistic models of where the aim is to maximize the profits of objects in a favourable candidate solutions. The main difference knapsack without exceeding its capacity. BKP is a between EDAs and evolutionary algorithms is that generalization of 0/1 knapsack problem in which multiple instances of distinct items but a single knapsack is taken. Real evolutionary algorithms produces new candidate solutions life applications of this problem include cryptography, using an implicit distribution defined by variation finance, etc. This paper proposes an estimation of distribution operators like crossover, mutation, whereas EDAs use algorithm (EDA) using greedy operator approach to find an explicit probability distribution model such as Bayesian solution to the BKP. EDA is stochastic optimization technique network, a multivariate normal distribution etc. EDAs can that explores the space of potential solutions by modelling be used to solve optimization problems like other probabilistic models of promising candidate solutions. conventional evolutionary algorithms. The rest of the paper is organized as follows: next section Index Terms— Bounded Knapsack, Greedy Algorithm, deals with the related work followed by the section III Estimation of Distribution Algorithm, Combinatorial Problem, which contains theoretical procedure of EDA. -
About Emotions There Are 8 Primary Emotions. You Are Born with These
About Emotions There are 8 primary emotions. You are born with these emotions wired into your brain. That wiring causes your body to react in certain ways and for you to have certain urges when the emotion arises. Here is a list of primary emotions: Eight Primary Emotions Anger: fury, outrage, wrath, irritability, hostility, resentment and violence. Sadness: grief, sorrow, gloom, melancholy, despair, loneliness, and depression. Fear: anxiety, apprehension, nervousness, dread, fright, and panic. Joy: enjoyment, happiness, relief, bliss, delight, pride, thrill, and ecstasy. Interest: acceptance, friendliness, trust, kindness, affection, love, and devotion. Surprise: shock, astonishment, amazement, astound, and wonder. Disgust: contempt, disdain, scorn, aversion, distaste, and revulsion. Shame: guilt, embarrassment, chagrin, remorse, regret, and contrition. All other emotions are made up by combining these basic 8 emotions. Sometimes we have secondary emotions, an emotional reaction to an emotion. We learn these. Some examples of these are: o Feeling shame when you get angry. o Feeling angry when you have a shame response (e.g., hurt feelings). o Feeling fear when you get angry (maybe you’ve been punished for anger). There are many more. These are NOT wired into our bodies and brains, but are learned from our families, our culture, and others. When you have a secondary emotion, the key is to figure out what the primary emotion, the feeling at the root of your reaction is, so that you can take an action that is most helpful. . -
Functional Aspects of Emotions in Fish
Behavioural Processes 100 (2013) 153–159 Contents lists available at ScienceDirect Behavioural Processes jou rnal homepage: www.elsevier.com/locate/behavproc Functional aspects of emotions in fish ∗ Silje Kittilsen Norwegian School of Veterinary Science, Norway a r t i c l e i n f o a b s t r a c t Article history: There is an ongoing scientific discussion on whether fish have emotions, and if so how they experience Received 19 March 2013 them? The discussion has incorporated important areas such as brain anatomy and function, physiological Received in revised form and behavioural responses, and the cognitive abilities that fish possess. Little attention has however, been 10 September 2013 directed towards what functional aspects emotions ought to have in fish. If fish have emotions – why? Accepted 11 September 2013 The elucidation of this question and an assessment of the scientific evidences of emotions in fish in an evolutionary and functional framework would represent a valuable contribution in the discussion on Keywords: whether fish are emotional creatures. Here parts of the vast amount of literature from both biology and Emotions Behaviour psychology relating to the scientific field of emotions, animal emotion, and the functional aspects that Cognition emotions fulfil in the lives of humans and animals are reviewed. Subsequently, by viewing fish behaviour, Psychology physiology and cognitive abilities in the light of this functional framework it is possible to infer what Fish functions emotions may serve in fish. This approach may contribute to the vital running discussion on Evolution the subject of emotions in fish. In fact, if it can be substantiated that emotions are likely to serve a function in fish similar to that of other higher vertebrate species, the notion that fish do have emotions will be strengthened. -
Greedy Algorithms Greedy Strategy: Make a Locally Optimal Choice, Or Simply, What Appears Best at the Moment R
Overview Kruskal Dijkstra Human Compression Overview Kruskal Dijkstra Human Compression Overview One of the strategies used to solve optimization problems CSE 548: (Design and) Analysis of Algorithms Multiple solutions exist; pick one of low (or least) cost Greedy Algorithms Greedy strategy: make a locally optimal choice, or simply, what appears best at the moment R. Sekar Often, locally optimality 6) global optimality So, use with a great deal of care Always need to prove optimality If it is unpredictable, why use it? It simplifies the task! 1 / 35 2 / 35 Overview Kruskal Dijkstra Human Compression Overview Kruskal Dijkstra Human Compression Making change When does a Greedy algorithm work? Given coins of denominations 25cj, 10cj, 5cj and 1cj, make change for x cents (0 < x < 100) using minimum number of coins. Greedy choice property Greedy solution The greedy (i.e., locally optimal) choice is always consistent with makeChange(x) some (globally) optimal solution if (x = 0) return What does this mean for the coin change problem? Let y be the largest denomination that satisfies y ≤ x Optimal substructure Issue bx=yc coins of denomination y The optimal solution contains optimal solutions to subproblems. makeChange(x mod y) Implies that a greedy algorithm can invoke itself recursively after Show that it is optimal making a greedy choice. Is it optimal for arbitrary denominations? 3 / 35 4 / 35 Chapter 5 Overview Kruskal Dijkstra Human Compression Overview Kruskal Dijkstra Human Compression Knapsack Problem Greedy algorithmsFractional Knapsack A sack that can hold a maximum of x lbs Greedy choice property Proof by contradiction: Start with the assumption that there is an You have a choice of items you can pack in the sack optimal solution that does not include the greedy choice, and show a A game like chess can be won only by thinking ahead: a player who is focused entirely on Maximize the combined “value” of items in the sack contradiction. -
A New Look on Regret Regulation Process for Risky Decisions Nawel
“I should not have been so cautious!” A new look on regret regulation process for risky decisions Nawel AYADI Maîtres de conférences IAE de Toulouse, Université Toulouse I Capitole CRM, EAC-CNRS 5032 [email protected] Laurent BERTRANDIAS Maîtres de conférences IAE de Toulouse, Université Toulouse I Capitole CRM, EAC-CNRS 5032 [email protected] « Je n’aurais pas dû être aussi prudent ! » Un nouveau regard sur le processus de régulation du regret pour les décisions risquées Résumé Le consommateur essaie de réguler son émotion de regret en prenant des décisions capables de minimiser le regret ressenti. Les conséquences des décisions risquées sont inconnues amenant le consommateur à anticiper le regret éprouvé pour les différentes situations possibles. L’article suggère que le regret anticipé est ambivalent et distingue le classique regret anticipé d’une prise de risque excessive (ARERT) du regret anticipé d’une recherche excessive de sécurité (ARESS). Les résultats de notre expérience montrent que ces deux regrets exercent des effets contraires sur la prise de risque mais que ces effets sont modérés par l’intelligence émotionnelle du consommateur. Mots-clés : régulation du regret, regret anticipé, intelligence émotionnelle, prise de risque “I should not have been so cautious!” A new look on regret regulation process for risky decisions Abstract Consumers try to regulate regret by taking decisions which minimize regret experience. Risky decisions consequences are unknown leading consumers to anticipate regret for all possible situations. The article suggests that anticipated regret is ambivalent and distinguishes between “traditional” anticipated regret of an excessive risk-taking (ARERT) and anticipated regret of an excessive safety-seeking (ARESS). -
Types of Algorithms
Types of Algorithms Material adapted – courtesy of Prof. Dave Matuszek at UPENN Algorithm classification Algorithms that use a similar problem-solving approach can be grouped together This classification scheme is neither exhaustive nor disjoint The purpose is not to be able to classify an algorithm as one type or another, but to highlight the various ways in which a problem can be attacked 2 1 A short list of categories Algorithm types we will consider include: Simple recursive algorithms Divide and conquer algorithms Dynamic programming algorithms Greedy algorithms Brute force algorithms Randomized algorithms Note: we may even classify algorithms based on the type of problem they are trying to solve – example sorting algorithms, searching algorithms etc. 3 Simple recursive algorithms I A simple recursive algorithm: Solves the base cases directly Recurs with a simpler subproblem Does some extra work to convert the solution to the simpler subproblem into a solution to the given problem We call these “simple” because several of the other algorithm types are inherently recursive 4 2 Example recursive algorithms To count the number of elements in a list: If the list is empty, return zero; otherwise, Step past the first element, and count the remaining elements in the list Add one to the result To test if a value occurs in a list: If the list is empty, return false; otherwise, If the first thing in the list is the given value, return true; otherwise Step past the first element, and test whether the value occurs in the remainder of the list 5 Backtracking algorithms Backtracking algorithms are based on a depth-first* recursive search A backtracking algorithm: Tests to see if a solution has been found, and if so, returns it; otherwise For each choice that can be made at this point, Make that choice Recur If the recursion returns a solution, return it If no choices remain, return failure *We will cover depth-first search very soon (once we get to tree-traversal and trees/graphs). -
Hybrid Metaheuristics Using Reinforcement Learning Applied to Salesman Traveling Problem
13 Hybrid Metaheuristics Using Reinforcement Learning Applied to Salesman Traveling Problem Francisco C. de Lima Junior1, Adrião D. Doria Neto2 and Jorge Dantas de Melo3 University of State of Rio Grande do Norte, Federal University of Rio Grande do Norte Natal – RN, Brazil 1. Introduction Techniques of optimization known as metaheuristics have achieved success in the resolution of many problems classified as NP-Hard. These methods use non deterministic approaches that reach very good solutions which, however, don’t guarantee the determination of the global optimum. Beyond the inherent difficulties related to the complexity that characterizes the optimization problems, the metaheuristics still face the dilemma of exploration/exploitation, which consists of choosing between a greedy search and a wider exploration of the solution space. A way to guide such algorithms during the searching of better solutions is supplying them with more knowledge of the problem through the use of a intelligent agent, able to recognize promising regions and also identify when they should diversify the direction of the search. This way, this work proposes the use of Reinforcement Learning technique – Q-learning Algorithm (Sutton & Barto, 1998) - as exploration/exploitation strategy for the metaheuristics GRASP (Greedy Randomized Adaptive Search Procedure) (Feo & Resende, 1995) and Genetic Algorithm (R. Haupt & S. E. Haupt, 2004). The GRASP metaheuristic uses Q-learning instead of the traditional greedy- random algorithm in the construction phase. This replacement has the purpose of improving the quality of the initial solutions that are used in the local search phase of the GRASP, and also provides for the metaheuristic an adaptive memory mechanism that allows the reuse of good previous decisions and also avoids the repetition of bad decisions. -
The Empathy Connection
The Empathy Connection Creating Caring Communities through the Human-Animal Relationship The Doris Day Animal Foundation (DDAF) is a national nonprofit organization working to create caring communities. Thanks to a generous grant from the Claire Giannini Fund, we are pleased to present “The Empathy Connection,” a publication designed to help parents, teachers, and other adults instill the important skill of empathy in our youth. As a mother of two school-age children, president of the parent teacher’s association of a middle school, and as the Executive Director of the Doris Day Animal Foundation, I know how important empathy is in children’s development. Empathy is an important skill, related to success in many areas of development—social, academic, and personal. Learning how to respond empathetically is also the best antidote to violence, bullying, and other unwanted, aggressive behavior in children. The basic tenet of DDAF’s “creating caring communities” mission is that the protection of, and respect for, animals is closely linked to human welfare. The development of empathy is a case in point: one of the best—and probably one of the most enjoyable—ways to teach children empathy is through the human-animal relationship. The Doris Day Animal Foundation offers training workshops and materials designed to help professional and lay communities address the problem of violence and promote positive development in children, families, and communities. We do this by demonstrating how paying attention to the animal-human welfare link builds safer, more creative communities for all living creatures. We hope you will let us know how you used “The Empathy Connection,” or other DDAF materials. -
Crisis Playbook—The Fear of Loss and Regret Approaching the Challenges and Potential Opportunities of Market Volatility
T. ROWE PRICE INSIGHTS ON GLOBAL EQUITY Crisis Playbook—The Fear of Loss and Regret Approaching the challenges and potential opportunities of market volatility. March 2020 isk happens. Global equity markets entered 2020 in a state Rof Goldilocks—low interest rates and bottoming short‑cycle economic indicators. Markets were heavily David Eiswert focused on the Trump administration’s election “put” and China’s focus on Portfolio Manager, economic stability heading into 2021. T. Rowe Price Global Stock Fund This benign environment drove markets and multiples higher. Then the butterfly flapped its wings and time, it seems that COVID‑19 is not a we found ourselves with COVID‑19, the mortal threat to most people. Obviously, novel coronavirus. this is conjecture given I am no expert in virology or public health. So we are COVID‑19 introduced a risk poorly continuing to track the development handled by monetary policy, the world’s of this crisis, learning and evolving our preferred tool for economic stability. A thinking as the situation unfolds. supply shock has spread outward from China along with the virus. Regardless, a virus that is novel and spreads far and wide means panic The virus appears to be very contagious as well as volatility in asset prices as but relatively mild compared with others, unexpected risks present themselves. Our first task such as Ebola and MERS, but pandemic We are tasked with managing a global means suffering and death and must equity fund given these circumstances, is to act early. be urgently addressed. The immediate and in moments such as this, I am control of COVID‑19 is particularly incredibly grateful for our global complex in that many of its victims are research resources. -
A Theory of Regret Regulation 1.0
JOURNAL OF CONSUMER PSYCHOLOGY, 17(1), 3–18 Copyright © 2007, Lawrence Erlbaum Associates, Inc. HJCP A Theory of Regret Regulation 1.0 Regret Regulation Theory Marcel Zeelenberg and Rik Pieters Tilburg University, The Netherlands We propose a theory of regret regulation that distinguishes regret from related emotions, specifies the conditions under which regret is felt, the aspects of the decision that are regret- ted, and the behavioral implications. The theory incorporates hitherto scattered findings and ideas from psychology, economics, marketing, and related disciplines. By identifying strate- gies that consumers may employ to regulate anticipated and experienced regret, the theory identifies gaps in our current knowledge and thereby outlines opportunities for future research. The average consumer makes a couple of thousands of concerning regret. Regret research originated in basic decisions daily. These include not only the decision of research in economics (Bell, 1982; Loomes & Sugden, 1982), which products and brands to buy and in which quantity, and psychology (Gilovich & Medvec, 1995; Kahneman & but also what to eat for breakfast, what kind of tea to Tversky, 1982; Landman, 1993). Nowadays one can find drink, whether to read the morning newspaper, which spe- examples of regret research in many different domains, such cific articles and ads (and whether to act on them), when to as marketing (Inman & McAlister, 1994; Simonson, 1992), stop reading, which shows to watch on which TV-channels law (Guthrie, 1999; Prentice & Koehler, 2003), organiza- (and whether to zap or zip), or maybe watch a DVD, and tional behavior (Goerke, Moller, & Schulz-Hardt, 2004; for how long, how to commute to work or school, and so Maitlis & Ozcelik, 2004), medicine (Brehaut et al., 2003; forth.