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##Human-Competitiveness DefinitionEvolving Hyper-Heuristics using Genetic Programming Rush-Hour (bronze Humies prize in 2009 by Ami Hauptman)Freecell(gold Humies prize in 2011 by Achiya Elyasaf)Other ExamplesHuman Competitive Results of Evolutionary ComputationOutline##What is Human competitive?John Koza defined Human-Competitiveness in his book: Genetic Algorithms IV (2003).There are 8 criteria by which a result can be considered Human-Competitive.(will be explained in next slide)Our mission: Creation of Human-Competitive innovative solutions by means of Evolution.##The 8 Criteria of Koza for Human Competitivenes.(A)result is a Patent from the past, improvement of a patent. A New patent.

##(B)result is equal to or better than another result that was published in a journal.

The 8 Criteria of Koza for Human Competitivenes.##(C)result is equal to or better than a result in a known DB of results.

The 8 Criteria of Koza for Human Competitivenes.##(D) publishable in its own right as a new scientific result.independentof the fact that the result was mechanically created.

The 8 Criteria of Koza for Human Competitivenes.##(E)The result is equal to or better than the best human-created solution.

The 8 Criteria of Koza for Human Competitivenes.##(F)equal to or better than an human achievement in its field at the time it was first discovered.

The 8 Criteria of Koza for Human Competitivenes.##(G)The result solves a problem of indisputable difficulty in its field.

The 8 Criteria of Koza for Human Competitivenes.##(H)The result holds its own or wins a regulated competition involving human contestants (in the form of either live human players or human-written computer programs).

The 8 Criteria of Koza for Human Competitivenes.##Humies CompetitionHumies annual Competition gives $$$ for the best HC results. (in GECCO conference)Awarding a gold, silver and bronze prizes to the best entries. (money $$$)BGU won 1 gold, 1 silver and 6 bronze prizes since 2005.I counted more than 75 Human-Competitive results on the Humies competition site.

http://www.sigevo.org/gecco-2012/http://www.genetic-programming.org/hc2011/combined.html

##Evolving Hyper-Heuristics using Genetic ProgrammingAmi Hauptman and Achiya Elyasaf#13OverviewIntroductionSearching Games State-GraphsUninformed SearchHeuristicsInformed SearchEvolving HeuristicsTest CasesRush HourFreeCell##Representing Games as State-GraphsEvery puzzle/game can be represented as a state graph:In puzzles, board games etc., every piece move can be counted as an edge/transition between statesIn computer war games etc. the place of the player / the enemy, all the parameters (health, shield) define a state

##Rush-Hour as a state-graph

Move blueMove purple##Searching Games State-GraphsUninformed/nave SearchBFS Breadth First SearchOptimal solutionExponential space in the search depthDFS Depth First Search(without node coloring). We might never track down the right path.Usually games contain cyclesLinear SpaceIterative Deepening: Combination of BFS & DFSEach iteration DFS with a depth limit is performed.Limit grows from one iteration to anotherWorst case - traverse the entire graph##Searching Games State-GraphsUninformed SearchMost of the game domains are PSPACE-Complete!Worst case - traverse the entire graphWe need an informed-search! (or an intelligent approach to traversing the graph)##Searching Games State-GraphsHeuristicsHeuristic function h:states -> Real. For every state s, h(s) is an estimation of the minimal distance/cost from s to a solutionIn case h is perfect: an informed search that tries states with the lowest h-value first will simply stroll to a solutionFor hard problems, finding a good h is hardBad heuristic means the search might never track down the solution

We need a good heuristic function to guide the informed search##Searching Games State-Graphs Informed SearchBest-First search: Like DFS but select nodes with higher heuristic value firstNot necessarily optimal##Searching Games State-Graphs Informed SearchA*: G(s)=cost from root till sH(s)=Heuristic estimationF(s)=G(s)+H(s)Holds closed and sorted open lists(the list of states needs to be checked out). Best (=lowest F(s)) node of all open nodes is selected.##Searching Games State-Graphs Informed Search (Cont.)IDA*: Iterative-Deepening with A*The expanded nodes are pushed to the DFS stack by descending heuristic valuesLet g(s) be the cost to reach state s from root: Only nodes with f(s)=g(s)+h(s)