temp_en_result.json

[
  {
    "question": "What is the field of study that considers genetic algorithms, evolution strategies, and genetic programming?",
    "options": [
      "Artificial intelligence",
      "Evolutionary computation",
      "Machine learning",
      "Natural selection"
    ],
    "answer": "B"
  },
  {
    "question": "According to Alan Turing, what is irrelevant to intelligence?",
    "options": [
      "The appearance of a system",
      "The capability to adapt",
      "The behaviour of a system",
      "The environment"
    ],
    "answer": "A"
  },
  {
    "question": "What is the result of simulating evolution on a computer?",
    "options": [
      "A series of optimisation algorithms",
      "A feasible solution",
      "An ever-changing environment",
      "Intelligent behaviour"
    ],
    "answer": "A"
  },
  {
    "question": "What can be considered as evidence of intelligent behaviour in humans?",
    "options": [
      "The capability to adapt",
      "The appearance of a system",
      "The process of evolution",
      "Everyday experience"
    ],
    "answer": "D"
  },
  {
    "question": "What is the process that simulates evolution by using selection, mutation, and reproduction?",
    "options": [
      "Genetic algorithms",
      "Evolution strategies",
      "Genetic programming",
      "Evolutionary computation"
    ],
    "answer": "D"
  },
  {
    "question": "What are the processes that neo-Darwinism is based on?",
    "options": [
      "Reproduction, mutation, competition, and selection",
      "Adaptation, mutation, competition, and selection",
      "Reproduction, adaptation, competition, and selection",
      "Reproduction, mutation, adaptation, and selection"
    ],
    "answer": "A"
  },
  {
    "question": "What is the essential property of life that guarantees the power to mutate?",
    "options": [
      "Reproduction",
      "Competition",
      "Selection",
      "Mutation"
    ],
    "answer": "A"
  },
  {
    "question": "What is being optimized by evolution in natural life?",
    "options": [
      "Reproduction",
      "Mutation",
      "Competition",
      "Evolutionary fitness"
    ],
    "answer": "D"
  },
  {
    "question": "What is the measure of an organism's ability to predict environmental changes and respond adequately?",
    "options": [
      "Evolutionary fitness",
      "Adaptive topology",
      "Optimised fitness",
      "Quantitative measure"
    ],
    "answer": "A"
  },
  {
    "question": "What does adaptive topology simulate?",
    "options": [
      "The fact that the environment is static",
      "The continuous function of fitness",
      "The changing shape of the topology",
      "The selection of species"
    ],
    "answer": "C"
  },
  {
    "question": "What is the goal of evolution?",
    "options": [
      "To generate a population of individuals with increasing fitness",
      "To simulate natural selection and genetics",
      "To optimise survival in a changing environment",
      "To create intelligent behaviour"
    ],
    "answer": "A"
  },
  {
    "question": "What is the explanation provided by Michalewicz for generating a population with increasing fitness?",
    "options": [
      "The breeding of rabbits with superior fitness",
      "The survival of faster rabbits",
      "The combination of genes from fast rabbits",
      "The environmental challenges faced by rabbits"
    ],
    "answer": "C"
  },
  {
    "question": "What is the purpose of simulating natural evolution in a computer?",
    "options": [
      "To create a population of individuals",
      "To evaluate their fitness",
      "To generate a new population through genetic operations",
      "To repeat the process a number of times"
    ],
    "answer": "D"
  },
  {
    "question": "What did John Holland aim to do with genetic algorithms?",
    "options": [
      "Make computers do what nature does",
      "Manipulate strings of binary digits",
      "Create a population of artificial chromosomes",
      "Measure the performance of chromosomes"
    ],
    "answer": "A"
  },
  {
    "question": "How is encoding carried out in genetic algorithms?",
    "options": [
      "By representing chromosomes as strings of ones and zeros",
      "By exchanging parts of two single chromosomes",
      "By changing the gene value in some randomly chosen location",
      "By measuring the performance of chromosomes"
    ],
    "answer": "A"
  },
  {
    "question": "What is the role of an evaluation function in genetic algorithms?",
    "options": [
      "To measure the performance of chromosomes",
      "To select chromosomes for mating",
      "To create offspring chromosomes",
      "To replace the initial chromosome population"
    ],
    "answer": "A"
  },
  {
    "question": "What is the termination criterion for genetic algorithms?",
    "options": [
      "A specified number of generations",
      "A stable fitness of the population",
      "The appearance of a superior chromosome",
      "The size of the initial population"
    ],
    "answer": "A"
  },
  {
    "question": "What is the fitness function in the example?",
    "options": [
      "f(x) = 15x",
      "f(x) = 15x^2",
      "f(x) = 15x * x^2",
      "f(x) = 15x + x^2"
    ],
    "answer": "B"
  },
  {
    "question": "What is the size of the chromosome population in the example?",
    "options": [
      "4",
      "6",
      "8",
      "10"
    ],
    "answer": "B"
  },
  {
    "question": "What is the crossover probability in the example?",
    "options": [
      "0.1",
      "0.5",
      "0.7",
      "0.9"
    ],
    "answer": "C"
  },
  {
    "question": "What is the mutation probability in the example?",
    "options": [
      "0.001",
      "0.01",
      "0.1",
      "1"
    ],
    "answer": "A"
  },
  {
    "question": "What is the role of mutation in genetic algorithms?",
    "options": [
      "To improve the average fitness of the population",
      "To provide a guarantee that the search algorithm is not trapped on a local optimum",
      "To create new offspring through crossover",
      "To select the fittest species for breeding"
    ],
    "answer": "B"
  },
  {
    "question": "What is the purpose of decoding in a genetic algorithm?",
    "options": [
      "To calculate the fitness of each chromosome",
      "To convert binary strings into decimal values",
      "To randomly generate an initial population",
      "To represent problem variables as a chromosome"
    ],
    "answer": "B"
  },
  {
    "question": "What is the range of integers that can be handled by 8-bits?",
    "options": [
      "0 to 255",
      "0 to 127",
      "0 to 63",
      "0 to 31"
    ],
    "answer": "A"
  },
  {
    "question": "What is the purpose of crossover in a genetic algorithm?",
    "options": [
      "To calculate the fitness of each chromosome",
      "To recombine features of the best chromosomes",
      "To randomly generate an initial population",
      "To convert binary strings into decimal values"
    ],
    "answer": "B"
  },
  {
    "question": "What is the purpose of mutation in a genetic algorithm?",
    "options": [
      "To calculate the fitness of each chromosome",
      "To convert binary strings into decimal values",
      "To randomly generate an initial population",
      "To explore the landscape in a random manner"
    ],
    "answer": "D"
  },
  {
    "question": "What is the purpose of performance graphs in a genetic algorithm?",
    "options": [
      "To calculate the fitness of each chromosome",
      "To represent problem variables as a chromosome",
      "To display the GA's performance",
      "To convert binary strings into decimal values"
    ],
    "answer": "C"
  },
  {
    "question": "What is the relationship between a schema and a chromosome?",
    "options": [
      "A schema is a set of bit strings that match the corresponding positions in a chromosome.",
      "A schema is a set of bit strings that do not match the corresponding positions in a chromosome.",
      "A schema is a set of bit strings that have fixed positions in a chromosome.",
      "A schema is a set of bit strings that have wild cards in a chromosome."
    ],
    "answer": "A"
  },
  {
    "question": "What is the order of a schema?",
    "options": [
      "The number of defined bits in a schema.",
      "The number of asterisks in a schema.",
      "The number of chromosomes in a schema.",
      "The number of instances of a schema."
    ],
    "answer": "A"
  },
  {
    "question": "According to the Schema Theorem, what can we predict about the presence of a given schema in the next chromosome generation?",
    "options": [
      "The presence of a given schema will increase in the next generation.",
      "The presence of a given schema will decrease in the next generation.",
      "The presence of a given schema will remain the same in the next generation.",
      "The presence of a given schema cannot be predicted."
    ],
    "answer": "A"
  },
  {
    "question": "What is the probability that a schema will survive after crossover?",
    "options": [
      "The probability is higher for short schemata than for long ones.",
      "The probability is higher for long schemata than for short ones.",
      "The probability is the same for all schemata.",
      "The probability depends on the order of the schema."
    ],
    "answer": "A"
  },
  {
    "question": "What is the probability that a schema will survive after mutation?",
    "options": [
      "The probability is higher for low-order schemata than for high-order ones.",
      "The probability is higher for high-order schemata than for low-order ones.",
      "The probability is the same for all schemata.",
      "The probability depends on the defining length of the schema."
    ],
    "answer": "A"
  },
  {
    "question": "Why are scheduling problems so difficult?",
    "options": [
      "They are likely to be unmanageable and cannot be solved by combinatorial search techniques.",
      "Heuristics alone cannot guarantee the best solution.",
      "They involve a competition for limited resources and are complicated by many constraints.",
      "All of the above."
    ],
    "answer": "D"
  },
  {
    "question": "What is the key to the success of the genetic algorithm in scheduling problems?",
    "options": [
      "Defining a fitness function that incorporates all the constraints.",
      "Using heuristics alone to guarantee the best solution.",
      "Solving the problem using combinatorial search techniques.",
      "None of the above."
    ],
    "answer": "A"
  },
  {
    "question": "What is the purpose of maintenance scheduling in power systems?",
    "options": [
      "To maximize the security of the power system.",
      "To minimize the net reserve of the power system.",
      "To predict the maximum load forecast during the maintenance period.",
      "To schedule outages of power units over a given period of time."
    ],
    "answer": "A"
  },
  {
    "question": "What is the net reserve of a power system?",
    "options": [
      "The total installed generating capacity of the system minus the power lost due to a scheduled outage.",
      "The maximum load forecast during the maintenance period.",
      "The total installed generating capacity of the system.",
      "The power lost due to a scheduled outage."
    ],
    "answer": "A"
  },
  {
    "question": "What are the constraints for the maintenance scheduling problem in power systems?",
    "options": [
      "Maintenance of any unit starts at the beginning of an interval and finishes at the end of the same or adjacent interval.",
      "The net reserve of the power system must be greater than or equal to zero at any interval.",
      "Both A and B.",
      "None of the above."
    ],
    "answer": "C"
  },
  {
    "question": "How is the scheduling problem represented as a chromosome in the genetic algorithm?",
    "options": [
      "Each unit is assigned a binary number and the chromosome is a sequence of these binary numbers.",
      "Each unit is represented as a 4-bit string, where each bit represents a maintenance interval.",
      "Each unit is represented as a fixed-length chromosome.",
      "None of the above."
    ],
    "answer": "B"
  },
  {
    "question": "What is the smallest indivisible part of the chromosome in this problem?",
    "options": [
      "A 1-bit string",
      "A 4-bit string",
      "A 7-bit string",
      "A 28-bit string"
    ],
    "answer": "B"
  },
  {
    "question": "What is the purpose of the fitness function in the genetic algorithm?",
    "options": [
      "To determine the number of intervals required for maintenance",
      "To evaluate the chromosome performance",
      "To select the genes for each unit",
      "To create an initial population of chromosomes"
    ],
    "answer": "B"
  },
  {
    "question": "How is the fitness of a chromosome determined?",
    "options": [
      "By counting the number of constraint violations",
      "By subtracting the sum of capacities from the total installed capacity",
      "By subtracting the maximum loads from the net reserves",
      "By taking the lowest net reserve value"
    ],
    "answer": "D"
  },
  {
    "question": "What happens if the net reserve at any interval is negative?",
    "options": [
      "The chromosome is considered illegal",
      "The chromosome's fitness remains unchanged",
      "The chromosome is selected for mating",
      "The chromosome is mutated"
    ],
    "answer": "A"
  },
  {
    "question": "What is the purpose of the crossover operator in the genetic algorithm?",
    "options": [
      "To randomly select genes from the corresponding pools",
      "To evaluate the chromosome performance",
      "To create an initial population of chromosomes",
      "To exchange parental genes and create children"
    ],
    "answer": "D"
  },
  {
    "question": "What is the purpose of the mutation operator in the genetic algorithm?",
    "options": [
      "To randomly select genes from the corresponding pools",
      "To evaluate the chromosome performance",
      "To create an initial population of chromosomes",
      "To randomly change a gene in a chromosome"
    ],
    "answer": "D"
  },
  {
    "question": "What factors should be considered when choosing the population size and number of generations in the genetic algorithm?",
    "options": [
      "The problem being solved and the problem coding scheme",
      "The number of units and intervals in the maintenance schedule",
      "The fitness function and the crossover operator",
      "The mutation rate and the gene pools"
    ],
    "answer": "A"
  },
  {
    "question": "What is the minimum net reserve for the best schedule created by 50 generations of 20 chromosomes?",
    "options": [
      "10 MW",
      "15 MW",
      "20 MW",
      "25 MW"
    ],
    "answer": "B"
  },
  {
    "question": "What is the minimum net reserve for the best schedule created by 100 generations of 20 chromosomes?",
    "options": [
      "10 MW",
      "15 MW",
      "20 MW",
      "25 MW"
    ],
    "answer": "C"
  },
  {
    "question": "What is the minimum net reserve for the best schedule after increasing the mutation rate to 0.01?",
    "options": [
      "10 MW",
      "15 MW",
      "20 MW",
      "25 MW"
    ],
    "answer": "D"
  },
  {
    "question": "What approach was proposed in Germany in the early 1960s to simulate natural evolution?",
    "options": [
      "Genetic algorithms",
      "Evolution strategies",
      "Analytical objective function",
      "Conventional optimisation method"
    ],
    "answer": "B"
  },
  {
    "question": "What is the simplest form of an evolution strategy?",
    "options": [
      "(1+1)-evolution strategy",
      "(1+1)+evolution strategy",
      "(1-1)-evolution strategy",
      "(1-1)+evolution strategy"
    ],
    "answer": "A"
  },
  {
    "question": "Why do we vary all the parameters simultaneously when generating a new solution in an evolution strategy?",
    "options": [
      "To reflect the nature of a chromosome",
      "To simplify the optimization process",
      "To reduce the computational complexity",
      "To increase the mutation rate"
    ],
    "answer": "A"
  },
  {
    "question": "What is the principal difference between a genetic algorithm and an evolution strategy?",
    "options": [
      "Genetic algorithms use only mutation, while evolution strategies use both crossover and mutation",
      "Genetic algorithms use both crossover and mutation, while evolution strategies use only mutation",
      "Genetic algorithms represent the problem in a coded form, while evolution strategies do not",
      "Genetic algorithms produce better results than evolution strategies"
    ],
    "answer": "B"
  },
  {
    "question": "What is the goal of genetic programming?",
    "options": [
      "To evolve a bit-string representation of a problem",
      "To create computer programs as the solution",
      "To search the space of possible computer programs",
      "To manipulate programs by applying genetic operators"
    ],
    "answer": "B"
  },
  {
    "question": "Which programming language is chosen as the main language for genetic programming?",
    "options": [
      "FORTRAN",
      "LISP",
      "C++",
      "Python"
    ],
    "answer": "B"
  },
  {
    "question": "What are the basic data structures in LISP?",
    "options": [
      "Atoms and lists",
      "Numbers and strings",
      "Functions and terminals",
      "Branches and leaves"
    ],
    "answer": "A"
  },
  {
    "question": "How are LISP S-expressions depicted?",
    "options": [
      "As a sequence of operations and values",
      "As a rooted point-labelled tree with ordered branches",
      "As a collection of items inside a pair of parentheses",
      "As a combination of functions and terminals"
    ],
    "answer": "B"
  },
  {
    "question": "What are the preparatory steps before applying genetic programming to a problem?",
    "options": [
      "Determine the set of terminals, select the set of primitive functions, define the fitness function, decide on the parameters for controlling the run, choose the method for designating a result of the run",
      "Choose the method for designating a result of the run, define the fitness function, select the set of primitive functions, determine the set of terminals, decide on the parameters for controlling the run",
      "Decide on the parameters for controlling the run, determine the set of terminals, define the fitness function, select the set of primitive functions, choose the method for designating a result of the run",
      "Select the set of primitive functions, determine the set of terminals, define the fitness function, decide on the parameters for controlling the run, choose the method for designating a result of the run"
    ],
    "answer": "A"
  },
  {
    "question": "What are the primary parameters used for controlling a run in genetic programming?",
    "options": [
      "Population size and maximum number of generations",
      "Fitness function and crossover operator",
      "Mutation operator and initial population",
      "Cloning operator and termination criterion"
    ],
    "answer": "A"
  },
  {
    "question": "What is the common practice in genetic programming for designating the result of a run?",
    "options": [
      "Selecting the fittest individual",
      "Choosing the largest computer program",
      "Randomly selecting a program",
      "Copying the initial population"
    ],
    "answer": "A"
  },
  {
    "question": "What are the functions and terminals used in computer programs in genetic programming?",
    "options": [
      "+, -, *, /, sqrt, a, b",
      "sin, cos, tan, log, x, y",
      "if, else, while, for, true, false",
      "print, input, int, float, str, list"
    ],
    "answer": "A"
  },
  {
    "question": "Is the crossover operator capable of operating on computer programs?",
    "options": [
      "Yes",
      "No",
      "It depends on the size of the programs",
      "Only if the programs have the same fitness"
    ],
    "answer": "A"
  },
  {
    "question": "What does the crossover operator do in genetic programming?",
    "options": [
      "Exchanges randomly chosen parts of two parents to create two offspring",
      "Replaces a function with a terminal in a computer program",
      "Randomly changes any function or terminal in a computer program",
      "Selects the fittest individual for reproduction"
    ],
    "answer": "A"
  },
  {
    "question": "Is mutation used in genetic programming?",
    "options": [
      "Yes",
      "No",
      "Only if the fitness of the program is poor",
      "Only if the program is too large"
    ],
    "answer": "A"
  },
  {
    "question": "What can be changed under mutation in genetic programming?",
    "options": [
      "Any function or any terminal",
      "Only functions",
      "Only terminals",
      "Only the fitness function"
    ],
    "answer": "A"
  },
  {
    "question": "What is the first step in genetic programming?",
    "options": [
      "Assign the maximum number of generations and probabilities",
      "Generate an initial population of computer programs",
      "Execute each computer program and calculate its fitness",
      "Select a genetic operator to perform cloning, crossover, or mutation"
    ],
    "answer": "A"
  },
  {
    "question": "What is the main advantage of genetic programming compared to genetic algorithms?",
    "options": [
      "Genetic programming uses high-level building blocks of variable length.",
      "Genetic programming works well in a large number of different cases.",
      "Genetic programming does not require a fixed-length coding.",
      "Genetic programming can be implemented in various programming languages."
    ],
    "answer": "C"
  },
  {
    "question": "What is a potential difficulty of genetic programming?",
    "options": [
      "Genetic programming may not scale up to more complex problems.",
      "Genetic programming requires extensive computer run times.",
      "Genetic programming is limited to LISP S-expressions.",
      "Genetic programming does not use evolutionary approach."
    ],
    "answer": "A"
  },
  {
    "question": "What are the main steps in developing a genetic algorithm?",
    "options": [
      "Create a population, evaluate their fitness, generate a new population, repeat.",
      "Create a population, evaluate their fitness, select the best individual, repeat.",
      "Create a population, evaluate their fitness, apply genetic operators, repeat.",
      "Create a population, evaluate their fitness, terminate the algorithm, repeat."
    ],
    "answer": "C"
  },
  {
    "question": "What is the purpose of the fitness function in genetic algorithms?",
    "options": [
      "To evaluate the performance of a chromosome.",
      "To generate a new population of individuals.",
      "To exchange parts of two chromosomes.",
      "To change the gene value in a chromosome."
    ],
    "answer": "A"
  },
  {
    "question": "What are evolution strategies used for?",
    "options": [
      "Technical optimization problems",
      "Analytical objective functions",
      "Conventional optimization methods",
      "Engineer's intuition"
    ],
    "answer": "A"
  },
  {
    "question": "What is the main difference between evolution strategies and genetic algorithms?",
    "options": [
      "Evolution strategies use only a mutation operator",
      "Evolution strategies require a coded form representation of a problem",
      "Genetic algorithms use only a mutation operator",
      "Genetic algorithms require a coded form representation of a problem"
    ],
    "answer": "A"
  },
  {
    "question": "What is genetic programming?",
    "options": [
      "A numerical optimization procedure",
      "A recent development in evolutionary computation",
      "A coding technique for genetic algorithms",
      "A computer program that solves a problem"
    ],
    "answer": "B"
  },
  {
    "question": "Why is LISP chosen as the main language for genetic programming?",
    "options": [
      "It allows manipulation of computer programs as data",
      "It is the most popular programming language",
      "It is the easiest language to learn",
      "It has the best performance for genetic programming"
    ],
    "answer": "A"
  },
  {
    "question": "Which book was written by Whitley?",
    "options": [
      "Foundations of Genetic Algorithms 2",
      "The Theory of Evolution Strategies",
      "Designing Efficient Parallel Genetic Algorithms",
      "The Design of Innovation: Lessons from and for Competent Genetic Algorithms"
    ],
    "answer": "A"
  },
  {
    "question": "In which city was the 6th International Congress on Genetics held?",
    "options": [
      "San Francisco, CA",
      "Ithaca, NY",
      "Boston",
      "Heidelberg"
    ],
    "answer": "B"
  },
  {
    "question": "Which book was written by Beyer?",
    "options": [
      "Noisy Optimization with Evolution Strategies",
      "The Theory of Evolution Strategies",
      "Designing Efficient Parallel Genetic Algorithms",
      "The Design of Innovation: Lessons from and for Competent Genetic Algorithms"
    ],
    "answer": "A"
  },
  {
    "question": "Which book was written by Cantu-Paz?",
    "options": [
      "Foundations of Genetic Algorithms 2",
      "The Theory of Evolution Strategies",
      "Designing Efficient Parallel Genetic Algorithms",
      "The Design of Innovation: Lessons from and for Competent Genetic Algorithms"
    ],
    "answer": "C"
  },
  {
    "question": "Which book was written by Christian?",
    "options": [
      "Illustrating Evolutionary Computation with Mathematica",
      "The Theory of Evolution Strategies",
      "Designing Efficient Parallel Genetic Algorithms",
      "The Design of Innovation: Lessons from and for Competent Genetic Algorithms"
    ],
    "answer": "A"
  },
  {
    "question": "Which book was written by Coley?",
    "options": [
      "An Introduction to Genetic Algorithms for Scientists and Engineers",
      "The Theory of Evolution Strategies",
      "Designing Efficient Parallel Genetic Algorithms",
      "The Design of Innovation: Lessons from and for Competent Genetic Algorithms"
    ],
    "answer": "A"
  },
  {
    "question": "Which book was written by Davidor?",
    "options": [
      "Genetic Algorithms and Robotics",
      "The Theory of Evolution Strategies",
      "Designing Efficient Parallel Genetic Algorithms",
      "The Design of Innovation: Lessons from and for Competent Genetic Algorithms"
    ],
    "answer": "A"
  },
  {
    "question": "Which book was written by Gen and Cheng?",
    "options": [
      "Genetic Algorithms and Engineering Design",
      "The Theory of Evolution Strategies",
      "Designing Efficient Parallel Genetic Algorithms",
      "The Design of Innovation: Lessons from and for Competent Genetic Algorithms"
    ],
    "answer": "A"
  },
  {
    "question": "Which book was written by Goldberg?",
    "options": [
      "The Design of Innovation: Lessons from and for Competent Genetic Algorithms",
      "The Theory of Evolution Strategies",
      "Designing Efficient Parallel Genetic Algorithms",
      "The Design of Innovation: Lessons from and for Competent Genetic Algorithms"
    ],
    "answer": "A"
  },
  {
    "question": "Which book was written by Haupt and Haupt?",
    "options": [
      "Practical Genetic Algorithms",
      "The Theory of Evolution Strategies",
      "Designing Efficient Parallel Genetic Algorithms",
      "The Design of Innovation: Lessons from and for Competent Genetic Algorithms"
    ],
    "answer": "A"
  },
  {
    "question": "Which book was written by Koza?",
    "options": [
      "Genetic Programming: On the Programming of Computers by Means of Natural Selection",
      "The Theory of Evolution Strategies",
      "Designing Efficient Parallel Genetic Algorithms",
      "The Design of Innovation: Lessons from and for Competent Genetic Algorithms"
    ],
    "answer": "A"
  },
  {
    "question": "Which book was written by Koza, Bennett III, Andre, and Keane?",
    "options": [
      "Genetic Programming III: Darwinian Invention and Problem Solving",
      "The Theory of Evolution Strategies",
      "Designing Efficient Parallel Genetic Algorithms",
      "The Design of Innovation: Lessons from and for Competent Genetic Algorithms"
    ],
    "answer": "A"
  },
  {
    "question": "Which book was written by Koza, Keane, Streeter, Mydlowec, Yu, and Lanza?",
    "options": [
      "Genetic Programming IV: Routine Human-Competitive Machine Intelligence",
      "The Theory of Evolution Strategies",
      "Designing Efficient Parallel Genetic Algorithms",
      "The Design of Innovation: Lessons from and for Competent Genetic Algorithms"
    ],
    "answer": "A"
  },
  {
    "question": "Which book was written by Langdon?",
    "options": [
      "Genetic Programming and Data Structures: Genetic Programming + Data Structures = Automatic Programming!",
      "The Theory of Evolution Strategies",
      "Designing Efficient Parallel Genetic Algorithms",
      "The Design of Innovation: Lessons from and for Competent Genetic Algorithms"
    ],
    "answer": "A"
  },
  {
    "question": "Which book was written by Langdon and Poli?",
    "options": [
      "Foundations of Genetic Programming",
      "The Theory of Evolution Strategies",
      "Designing Efficient Parallel Genetic Algorithms",
      "The Design of Innovation: Lessons from and for Competent Genetic Algorithms"
    ],
    "answer": "A"
  },
  {
    "question": "Which book was written by Man, Tang, Kwong, and Halang?",
    "options": [
      "Genetic Algorithms for Control and Signal Processing",
      "The Theory of Evolution Strategies",
      "Designing Efficient Parallel Genetic Algorithms",
      "The Design of Innovation: Lessons from and for Competent Genetic Algorithms"
    ],
    "answer": "A"
  },
  {
    "question": "Which book was written by Man, Tang, and Kwong?",
    "options": [
      "Genetic Algorithms: Concepts and Designs",
      "The Theory of Evolution Strategies",
      "Designing Efficient Parallel Genetic Algorithms",
      "The Design of Innovation: Lessons from and for Competent Genetic Algorithms"
    ],
    "answer": "A"
  },
  {
    "question": "Which book was written by O\u2019Neill and Ryan?",
    "options": [
      "Grammatical Evolution: Evolutionary Automatic Programming in an Arbitrary Language",
      "The Theory of Evolution Strategies",
      "Designing Efficient Parallel Genetic Algorithms",
      "The Design of Innovation: Lessons from and for Competent Genetic Algorithms"
    ],
    "answer": "A"
  }
]