Feb 19, 2019
diff --git a/Chapter5/genetic_algorithm.py b/Chapter5/genetic_algorithm.py

    # Use the probability distribution wheel to pick 2 parents
    # Note: will not work with negative fitness results
    def _pick_roulette(self, wheel: List[float]) -> Tuple[C,C]:
    def _pick_roulette(self, wheel: List[float]) -> Tuple[C,...]:
        return tuple(choices(self._population, weights=wheel, k=2))

    # Choose num_participants at random and take the best 2
    def _pick_tournament(self, num_participants: int) -> Tuple[C,C]:
    def _pick_tournament(self, num_participants: int) -> Tuple[C,...]:
        participants: List[C] = choices(self._population, k=num_participants)
        return tuple(nlargest(2, participants, key=self._fitness_key))

        while len(new_population) < len(self._population):
            # pick the 2 parents
            if self._selection_type == GeneticAlgorithm.SelectionType.ROULETTE:
                parents: Tuple[C,C] = self._pick_roulette([x.fitness() for x in self._population])
                parents: Tuple[C,...] = self._pick_roulette([x.fitness() for x in self._population])
            else:
                parents = self._pick_tournament(len(self._population) // 2)
            # potentially crossover the 2 parents
Original file line number	Diff line number	Diff line change
Expand Up		@@ -38,11 +38,11 @@ def __init__(self, initial_population: List[C], threshold: float, max_generation

		# Use the probability distribution wheel to pick 2 parents
		# Note: will not work with negative fitness results
		def _pick_roulette(self, wheel: List[float]) -> Tuple[C,C]:
		def _pick_roulette(self, wheel: List[float]) -> Tuple[C,...]:
		return tuple(choices(self._population, weights=wheel, k=2))

		# Choose num_participants at random and take the best 2
		def _pick_tournament(self, num_participants: int) -> Tuple[C,C]:
		def _pick_tournament(self, num_participants: int) -> Tuple[C,...]:
		participants: List[C] = choices(self._population, k=num_participants)
		return tuple(nlargest(2, participants, key=self._fitness_key))

Expand All		@@ -53,7 +53,7 @@ def _reproduce_and_replace(self) -> None:
		while len(new_population) < len(self._population):
		# pick the 2 parents
		if self._selection_type == GeneticAlgorithm.SelectionType.ROULETTE:
		parents: Tuple[C,C] = self._pick_roulette([x.fitness() for x in self._population])
		parents: Tuple[C,...] = self._pick_roulette([x.fitness() for x in self._population])
		else:
		parents = self._pick_tournament(len(self._population) // 2)
		# potentially crossover the 2 parents
Expand Down