International Business Machines Corporation

Jason Frederick Cantin

Steven R Kunkel

Robert H Bell, Jr

Michael Cracraft

William John Starke

James Stephen Fields, Jr

Sameh Sherif Sharkawi

Jeffrey A Stuecheli

Eric Eugene Retter

Donald R DeSota

Jack Robert Smith

Michael Ju Hyeok Lee

Richard Nicholas

Kenichi Tsuchiya

Arnold S Tran

Prasenjit Chakraborty

In some embodiments, a computerized method may comprise: determining, by an adaptive cataclysm controller operating on one or more processors, that the population of candidate solutions has converged during a run of the genetic algorithm; determining, by the adaptive cataclysm controller operating on the one or more processors, a cause for convergence of the population based, at least in part, on an analysis of the run of the genetic algorithm; selecting, by the adaptive cataclysm controller operating on the one or more processors, a first cataclysm strategy of a plurality of different cataclysm strategies based, at least in part, on one of the cause for the convergence and a history of the run of the genetic algorithm; and simulating, by the adaptive cataclysm controller operating on the one or more processors, a cataclysm based on the first cataclysm strategy.

Adaptive cataclysms in genetic algorithms

A tool computes fitness values for a first generation of a first sub-population of a plurality of sub-populations. A population of candidate solutions for an optimization problem was previously divided into the plurality of sub-populations. The population of candidate solutions was created for an iterative computing process in accordance with an evolutionary algorithm to identify a most fit candidate solution for the optimization problem. The tool determines a speculative ranking of the first generation of the first sub-population prior to the fitness values being computed for all candidate solutions in the first generation of the first sub-population. The tool generates a next generation of the first sub-population based, at least in part, on the speculative ranking prior to completion of computation of the fitness values for the first generation of the first sub-population.

Speculative asynchronous sub-population evolutionary computing

After beginning an evaluation of a first generation of a first plurality of candidate solutions and prior to completion of the evaluation, a system can determine a first plurality of tournament participants from the first plurality of candidate solutions The system may determine whether each of the first plurality of tournament participants have been evaluated. In response to determining each of the first plurality of tournament participants have been evaluated the system can determine a first one or more winners of the tournament and determine whether a first quantity of the first one or more winners is above a predefined threshold. In response to determining the first quantity is above a predefined threshold, the system can determine a second generation of a second plurality of candidate solutions, the second plurality of candidate solutions based, at least in part, on the first one or more winners.

Early generation of individuals to accelerate genetic algorithms

Figures of merit by actual design parameters are tracked over iterations for candidate solutions that include both actual design parameters and actual context parameters. Instead of returning a current iteration figure of merit, a worst observed figure of merit for a set of actual design parameters is returned as the figure of merit for a candidate solution. Since the candidate solution includes both actual design parameters and actual context parameters and the worst observed figures of merit are tracked by actual design parameters, the figure of merit for a set of design parameters will be the worst of the observed worst case scenarios as defined by the actual context parameters over a run of a metaheuristic optimizer.

Guiding metaheuristic to search for best of worst

It is determined that a population of candidate solutions for an optimization problem has prematurely converged during a metaheuristic optimization run. A cause for premature convergence of the population is determined based, at least in part, on an analysis of the metaheuristic optimization run. A first cataclysm strategy of a plurality of cataclysm strategies is selected based, at least in part, on one of the cause of the premature convergence and a history of the metaheuristic optimization run. A cataclysm is simulated based, at least in part, on the first cataclysm strategy.

Speculative asynchronous sub-population evolutionary computing utilizing a termination speculation threshold

While at least one candidate solution of a first generation of candidate solutions remains to be evaluated in accordance with a fitness function for an optimization problem, a plurality of candidate solutions is selected from the first generation of candidate solutions to participate in a tournament. It is determined whether each of the plurality of candidate solutions selected to participate in the tournament have been evaluated in accordance with the fitness function. If all have been evaluated, then one or more winners of the tournament are selected from the plurality of candidate solutions of the first generation of candidate solutions. A candidate solution of a second generation of candidate solutions is created with the selected one or more winners of the tournament in accordance with a genetic operator.

Figures of merit by actual design parameters are tracked over iterations for candidate solutions that include both actual design parameters and actual context parameters. Instead of returning a current iteration figure of merit, a worst observed figure of merit for a set of actual design parameters is returned as the figure of merit for a candidate solution. Since the candidate solution includes both actual design parameters and actual context parameters and the worst observed figures of merit are tracked by actual design parameters, the figure of merit for a set of design parameters wilt be the worst of the observed worst case scenarios as defined by the actual context parameters over a run of a metaheuristic optimizer.

Disclosed are a processor and a processing method incorporating an instruction pipeline with direction prediction (i.e., taken or not taken) for conditional branch instructions. In the embodiments, reading of a branch instruction history table (BHT) and a branch instruction target address cache (BTAC) for branch direction prediction occurs in parallel with the current instruction fetch in order to minimize delay in the next instruction fetch. Additionally, direction prediction is performed in the very next clock cycle based either on an initial direction prediction for the specific instruction, as stored in the BHT, or, if applicable, on a prior entry for the specific instruction in the BTAC. An override bit associated with each entry in the BTAC is the determining factor for whether or the BTAC or BHT is controlling. Override bits in the BTAC can be pre-established based on the branch instruction type in order to ensure prediction accuracy.

Processor and data processing method incorporating an instruction pipeline with conditional branch direction prediction for fast access to branch target instructions

A parallel genetic algorithm computing process tracks forward progress of a first sub-population across generations thereof. The first sub-population is one of a plurality of sub-populations that form a population of candidate solutions to an optimization problem. At a current generation of the first sub-population, it is determined that forward progress of the first sub-population fails a set of one or more forward progress criteria. In response to determining that the forward progress of the first sub-population fails the set of one or more forward progress criteria at the current generation, a local catastrophe is invoked on the current generation of the first sub-population. The first sub-population is re-populated after the local catastrophe is invoked. The first sub-population is re-established after re-populating while constraining migration to the first sub-population.

Using global and local catastrophes across sub-populations in parallel evolutionary computing

Some examples are directed to selecting at least one candidate solution from a first plurality of candidate solutions that has converged on a suboptimal solution during a computer simulation. The computer simulation tests fitness of the first plurality of candidate solutions for an optimization problem. Some examples are further direct to storing a copy of the at least one candidate solution, performing a cataclysm on the first plurality of candidate solutions, and generating a second plurality of candidate solutions. Some examples are further direct to integrating the copy of the at least one candidate solution into the second plurality of candidate solutions after performing of one or more additional computer simulations that test the fitness of the second plurality of candidate solutions for the optimization problem.

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Round Rock, TX, United States of America

Jason Frederick Cantin