Selection and Penalty Strategies for Genetic Algorithms Designed to Solve Spatial Forest Planning Problems

Table 4

Average solutions for landscapes A, B, and C. We present the average solution in terms of percent of trivial upper bound (TUB), for each selection/penalty combination. The secondary column heading represents the maximum number of generations. PROP: Proportional selection; BIN DT: Binary, deterministic tournament; 7 DT: A deterministic tournament with 7 competitors; U(2, 10) DT: A deterministic tournament with the number of competitors sampled from a uniform distribution on the range (2, 10); 2–7 DT: A deterministic tournament where the number of competitors increases from 2 to 7 throughout the course of the search.

Penalty	Selection	Landscape A		Landscape B		Landscape C
		750	1500	1500	3000	1500	6000
Static	PROP	83.85	89.13	75.11	85.99	65.08	85.78
	BIN DT	82.61	87.63	80.19	86.84	69.96	82.33
	7 DT	82.11	88.31	80.98	87.86	70.22	81.60
	U(2, 10) DT	81.98	88.71	80.83	87.11	70.94	81.93
	2–7 DT	82.23	87.77	80.59	87.42	70.39	82.28
Dynamic	PROP	85.38	89.78	75.94	86.41	66.83	88.00
	BIN DT	78.68	85.04	79.85	87.86	68.97	83.80
	7 DT	80.05	84.48	81.10	88.31	68.54	82.29
	U(2, 10) DT	78.80	86.03	81.17	88.20	69.41	83.08
	2–7 DT	77.86	86.29	80.87	87.84	69.56	82.48