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Large Language Models (LLMs) have emerged as transformative tools in artificial intelligence, capable of processing and understanding extensive human knowledge to enhance problem-solving across various domains. This paper explores the potential of LLMs to drive the discovery of symbolic solutions within scientific and engineering disciplines, where...
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Context 1
... only observed negative impact occurs in the Oscillation 2 problem, where the inclusion of specific, misleading knowledge from numpy.gradient adversely affects the solutions. An analysis of the knowledge library during iterations 500 to 1500 confirms the presence of this detrimental knowledge (Figure 8). Figure 8 illustrates the pollution within the knowledge pool for Oscillation 2, where irrelevant or incorrect knowledge entries, such as those related to numpy.gradient, have accumulated. ...
Context 2
... analysis of the knowledge library during iterations 500 to 1500 confirms the presence of this detrimental knowledge (Figure 8). Figure 8 illustrates the pollution within the knowledge pool for Oscillation 2, where irrelevant or incorrect knowledge entries, such as those related to numpy.gradient, have accumulated. so that many useless knowledge for performance improvement are summarized in the pool false. ...
Context 3
... only observed negative impact occurs in the Oscillation 2 problem, where the inclusion of specific, misleading knowledge from numpy.gradient adversely affects the solutions. An analysis of the knowledge library during iterations 500 to 1500 confirms the presence of this detrimental knowledge (Figure 8). Figure 8 illustrates the pollution within the knowledge pool for Oscillation 2, where irrelevant or incorrect knowledge entries, such as those related to numpy.gradient, have accumulated. ...
Context 4
... analysis of the knowledge library during iterations 500 to 1500 confirms the presence of this detrimental knowledge (Figure 8). Figure 8 illustrates the pollution within the knowledge pool for Oscillation 2, where irrelevant or incorrect knowledge entries, such as those related to numpy.gradient, have accumulated. so that many useless knowledge for performance improvement are summarized in the pool false. ...
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